Generative AI Engineer Certification Databricks-Generative-AI-Engineer-Associate Sample Questions Reliable
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NEW QUESTION # 35
A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output "In Stock" if the product is available or only the term "Out of Stock" if not.
Which prompt will work to allow the engineer to respond to call classification labels correctly?
- A. Respond with "Out of Stock" if the customer asks for a product.
- B. Respond with "In Stock" if the customer asks for a product.
- C. You will be given a customer call transcript where the customer asks about product availability. The outputs are either "In Stock" or "Out of Stock". Format the output in JSON, for example: {"call_id":
"123", "label": "In Stock"}. - D. You will be given a customer call transcript where the customer inquires about product availability.
Respond with "In Stock" if the product is available or "Out of Stock" if not.
Answer: C
Explanation:
* Problem Context: The Generative AI Engineer needs a prompt that will enable an LLM trained on customer call transcripts to classify and respond correctly regarding product availability. The desired response should clearly indicate whether a product is "In Stock" or "Out of Stock," and it should be formatted in a way that is structured and easy to parse programmatically, such as JSON.
* Explanation of Options:
* Option A: Respond with "In Stock" if the customer asks for a product. This prompt is too generic and does not specify how to handle the case when a product is not available, nor does it provide a structured output format.
* Option B: This option is correctly formatted and explicit. It instructs the LLM to respond based on the availability mentioned in the customer call transcript and to format the response in JSON.
This structure allows for easy integration into systems that may need to process this information automatically, such as customer service dashboards or databases.
* Option C: Respond with "Out of Stock" if the customer asks for a product. Like option A, this prompt is also insufficient as it only covers the scenario where a product is unavailable and does not provide a structured output.
* Option D: While this prompt correctly specifies how to respond based on product availability, it lacks the structured output format, making it less suitable for systems that require formatted data for further processing.
Given the requirements for clear, programmatically usable outputs,Option Bis the optimal choice because it provides precise instructions on how to respond and includes a JSON format example for structuring the output, which is ideal for automated systems or further data handling.
NEW QUESTION # 36
What is the most suitable library for building a multi-step LLM-based workflow?
- A. PySpark
- B. Pandas
- C. LangChain
- D. TensorFlow
Answer: C
Explanation:
* Problem Context: The Generative AI Engineer needs a tool to build amulti-step LLM-based workflow. This type of workflow often involves chaining multiple steps together, such as query generation, retrieval of information, response generation, and post-processing, with LLMs integrated at several points.
* Explanation of Options:
* Option A: Pandas: Pandas is a powerful data manipulation library for structured data analysis, but it is not designed for managing or orchestrating multi-step workflows, especially those involving LLMs.
* Option B: TensorFlow: TensorFlow is primarily used for training and deploying machine learning models, especially deep learning models. It is not designed for orchestrating multi-step tasks in LLM-based workflows.
* Option C: PySpark: PySpark is a distributed computing framework used for large-scale data processing. While useful for handling big data, it is not specialized for chaining LLM-based operations.
* Option D: LangChain: LangChain is a purpose-built framework designed specifically for orchestrating multi-step workflowswith large language models (LLMs). It enables developers to easily chain different tasks, such as retrieving documents, summarizing information, and generating responses, all in a structured flow. This makes it the best tool for building complex LLM-based workflows.
Thus,LangChainis the most suitable library for creating multi-step LLM-based workflows.
NEW QUESTION # 37
A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn't hallucinate or leak confidential data.
Which approach should NOT be used to mitigate hallucination or confidential data leakage?
- A. Use a strong system prompt to ensure the model aligns with your needs.
- B. Add guardrails to filter outputs from the LLM before it is shown to the user
- C. Fine-tune the model on your data, hoping it will learn what is appropriate and not
- D. Limit the data available based on the user's access level
Answer: C
Explanation:
When addressing concerns of hallucination and data leakage in an LLM application for internal company policies, fine-tuning the model on internal data with the hope it learns data boundaries can be problematic:
* Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There's a risk of overfitting to the specific data details, which might lead to unintended leakage.
* Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.
Better Approaches:
* A,C, andDinvolve setting up operational safeguards and constraints that directly address data leakage and ensure responses are aligned with specific user needs and security levels.
Fine-tuning lacks the targeted control needed for such sensitive applications and can introduce new risks, making it an unsuitable approach in this context.
NEW QUESTION # 38
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?
- A. Use any available data you personally created which is completely original and you can decide what license to use.
- B. Reach out to the data curators directly before you have started using the trained model to let them know.
- C. Only use data explicitly labeled with an open license and ensure the license terms are followed.
- D. Reach out to the data curators directly after you have started using the trained model to let them know.
Answer: D
Explanation:
* Problem Context: When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options:
* Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It's essential to clarify the licensing termsbeforeusing the data, not after.
Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.
NEW QUESTION # 39
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?
- A. BGE-large
- B. Llama2-70B
- C. OpenAI GPT-4
- D. Dolly 1.5B
Answer: A
Explanation:
Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors areconfidentialityandsensitivityof the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
Explanation of Options:
* Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.
* Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
* Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements.
Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.
* Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns,BGE-largeis assumed to be configurable for secure internal use, making it the optimal choice for this scenario.
NEW QUESTION # 40
A Generative AI Engineer is testing a simple prompt template in LangChain using the code below, but is getting an error.
Assuming the API key was properly defined, what change does the Generative AI Engineer need to make to fix their chain?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
To fix the error in the LangChain code provided for using a simple prompt template, the correct approach is Option C. Here's a detailed breakdown of why Option C is the right choice and how it addresses the issue:
* Proper Initialization: In Option C, the LLMChain is correctly initialized with the LLM instance specified as OpenAI(), which likely represents a language model (like GPT) from OpenAI. This is crucial as it specifies which model to use for generating responses.
* Correct Use of Classes and Methods:
* The PromptTemplate is defined with the correct format, specifying that adjective is a variable within the template. This allows dynamic insertion of values into the template when generating text.
* The prompt variable is properly linked with the PromptTemplate, and the final template string is passed correctly.
* The LLMChain correctly references the prompt and the initialized OpenAI() instance, ensuring that the template and the model are properly linked for generating output.
Why Other Options Are Incorrect:
* Option A: Misuses the parameter passing in generate method by incorrectly structuring the dictionary.
* Option B: Incorrectly uses prompt.format method which does not exist in the context of LLMChain and PromptTemplate configuration, resulting in potential errors.
* Option D: Incorrect order and setup in the initialization parameters for LLMChain, which would likely lead to a failure in recognizing the correct configuration for prompt and LLM usage.
Thus, Option C is correct because it ensures that the LangChain components are correctly set up and integrated, adhering to proper syntax and logical flow required by LangChain's architecture. This setup avoids common pitfalls such as type errors or method misuses, which are evident in other options.
NEW QUESTION # 41
A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?
- A. Response-generating LLM
- B. Invite users to submit long, rather than concise, questions
- C. Vector database
- D. Embedding model
Answer: B
Explanation:
The task involves building a question-answering chatbot for an automotive company using a database of vehicle-related documents. The chatbot must efficiently process customer inquiries and provide accurate responses. Let's evaluate each component to determine which isnotuseful, per Databricks Generative AI Engineer principles.
* Option A: Response-generating LLM
* An LLM is essential for generating natural language responses to customer queries based on retrieved information. This is a core component of any chatbot.
* Databricks Reference:"The response-generating LLM processes retrieved context to produce coherent answers"("Building LLM Applications with Databricks," 2023).
* Option B: Invite users to submit long, rather than concise, questions
* Encouraging long questions is a user interaction design choice, not a technical component of the chatbot's architecture. Moreover, long, verbose questions can complicate intent detection and retrieval, reducing efficiency and accuracy-counter to best practices for chatbot design. Concise questions are typically preferred for clarity and performance.
* Databricks Reference: While not explicitly stated, Databricks' "Generative AI Cookbook" emphasizes efficient query processing, implying that simpler, focused inputs improve LLM performance. Inviting long questions doesn't align with this.
* Option C: Vector database
* A vector database stores embeddings of the vehicle documents, enabling fast retrieval of relevant information via semantic search. This is critical for a question-answering system with a large document corpus.
* Databricks Reference:"Vector databases enable scalable retrieval of context from large datasets"("Databricks Generative AI Engineer Guide").
* Option D: Embedding model
* An embedding model converts text (documents and queries) into vector representations for similarity search. It's a foundational component for retrieval-augmented generation (RAG) in chatbots.
* Databricks Reference:"Embedding models transform text into vectors, facilitating efficient matching of queries to documents"("Building LLM-Powered Applications").
Conclusion: Option B is not a usefulcomponentin building the chatbot. It's a user-facing suggestion rather than a technical building block, and it could even degrade performance by introducing unnecessary complexity. Options A, C, and D are all integral to a Databricks-aligned chatbot architecture.
NEW QUESTION # 42
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?
- A. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.
- B. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
- C. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
- D. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
Answer: B
Explanation:
To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:
* Chunking and Vector Store Embedding:HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into avector store(a database that stores vector representations of text). Each chunk of text is transformed into an embeddingusing a transformer-based model, which allows for efficient similarity-based retrieval.
* Using Vector Search for Retrieval:When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.
* LLM to Generate a Response:Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.
* Why Other Options Are Less Suitable:
* A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.
* B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.
* C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.
Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.
NEW QUESTION # 43
A Generative Al Engineer is using an LLM to classify species of edible mushrooms based on text descriptions of certain features. The model is returning accurate responses in testing and the Generative Al Engineer is confident they have the correct list of possible labels, but the output frequently contains additional reasoning in the answer when the Generative Al Engineer only wants to return the label with no additional text.
Which action should they take to elicit the desired behavior from this LLM?
- A. Use a system prompt to instruct the model to be succinct in its answer
- B. Use zero shot prompting to instruct the model on expected output format
- C. Use few snot prompting to instruct the model on expected output format
- D. Use zero shot chain-of-thought prompting to prevent a verbose output format
Answer: A
Explanation:
The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let's assess how to control output format effectively.
* Option A: Use few shot prompting to instruct the model on expected output format
* Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.
* Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").
* Option B: Use zero shot prompting to instruct the model on expected output format
* Zero-shot prompting relies on a single instruction (e.g., "Return only the label") without examples. It's simpler than few-shot but may not consistently enforce succinctness if the LLM's default behavior is verbose.
* Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").
* Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format
* Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity-opposite to the desired outcome. This contradicts the goal of label-only output.
* Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").
* Option D: Use a system prompt to instruct the model to be succinct in its answer
* A system prompt (e.g., "Respond with only the species label, no additional text") sets a global instruction for the LLM's behavior. It's direct, reusable, and effective for controlling output style across queries.
* Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).
Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks' best practices for output control.
NEW QUESTION # 44
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?
- A. Increase the amount of compute that powers the LLM to process input faster
- B. Reduce the time that the users can interact with the LLM
- C. Ask the LLM to remind the user that the input is malicious but continue the conversation with the user
- D. Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist
Answer: D
Explanation:
In this case, the Generative AI Engineer is developing an application to generate personalized birthday poems, but there's a need to safeguard againstmalicious user inputs. The best solution is to implement asafety filter (option A) to detect harmful or inappropriate inputs.
* Safety Filter Implementation:Safety filters are essential for screening user input and preventing inappropriate content from being processed by the LLM. These filters can scan inputs for harmful language, offensive terms, or malicious content and intervene before the prompt is passed to the LLM.
* Graceful Handling of Harmful Inputs:Once the safety filter detects harmful content, the system can provide a message to the user, such as "I'm unable to assist with this request," instead of processing or responding to malicious input. This protects the system from generating harmful content and ensures a controlled interaction environment.
* Why Other Options Are Less Suitable:
* B (Reduce Interaction Time): Reducing the interaction time won't prevent malicious inputs from being entered.
* C (Continue the Conversation): While it's possible to acknowledge malicious input, it is not safe to continue the conversation with harmful content. This could lead to legal or reputational risks.
* D (Increase Compute Power): Adding more compute doesn't address the issue of harmful content and would only speed up processing without resolving safety concerns.
Therefore, implementing asafety filterthat blocks harmful inputs is the most effective technique for safeguarding the application.
NEW QUESTION # 45
A company has a typical RAG-enabled, customer-facing chatbot on its website.
Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.
- A. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
- B. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model
- C. 1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM
- D. 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM
Answer: D
Explanation:
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
* Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
* Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
* Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
* Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response- generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
* B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response- generating LLM, which is option A.
NEW QUESTION # 46
A company has a typical RAG-enabled, customer-facing chatbot on its website.
Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.
- A. 1.response-generating LLM, 2.vector search, 3.context-augmented prompt, 4.embedding model
- B. 1.response-generating LLM, 2.context-augmented prompt, 3.vector search, 4.embedding model
- C. 1.context-augmented prompt, 2.vector search, 3.embedding model, 4.response-generating LLM
- D. 1.embedding model, 2.vector search, 3.context-augmented prompt, 4.response-generating LLM
Answer: D
Explanation:
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
* Embedding Model (1):The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
* Vector Search (2):The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
* Context-Augmented Prompt (3):The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
* Response-Generating LLM (4):Finally, the context-augmented prompt is fed into a response- generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
* B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence isembedding model, vector search, context-augmented prompt, response- generating LLM, which is option A.
NEW QUESTION # 47
A Generative AI Engineer is building a Generative AI system that suggests the best matched employee team member to newly scoped projects. The team member is selected from a very large team. Thematch should be based upon project date availability and how well their employee profile matches the project scope. Both the employee profile and project scope are unstructured text.
How should the Generative Al Engineer architect their system?
- A. Create a tool for finding available team members given project dates. Embed team profiles into a vector store and use the project scope and filtering to perform retrieval to find the available best matched team members.
- B. Create a tool for finding available team members given project dates. Embed all project scopes into a vector store, perform a retrieval using team member profiles to find the best team member.
- C. Create a tool to find available team members given project dates. Create a second tool that can calculate a similarity score for a combination of team member profile and the project scope. Iterate through the team members and rank by best score to select a team member.
- D. Create a tool for finding team member availability given project dates, and another tool that uses an LLM to extract keywords from project scopes. Iterate through available team members' profiles and perform keyword matching to find the best available team member.
Answer: A
Explanation:
* Problem Context: The problem involves matching team members to new projects based on two main factors:
* Availability: Ensure the team members are available during the project dates.
* Profile-Project Match: Use the employee profiles (unstructured text) to find the best match for a project's scope (also unstructured text).
The two main inputs are theemployee profilesandproject scopes, both of which are unstructured. This means traditional rule-based systems (e.g., simple keyword matching) would be inefficient, especially when working with large datasets.
* Explanation of Options: Let's break down the provided options to understand why D is the most optimal answer.
* Option Asuggests embedding project scopes into a vector store and then performing retrieval using team member profiles. While embedding project scopes into a vector store is a valid technique, it skips an important detail: the focus should primarily be on embedding employee profiles because we're matching the profiles to a new project, not the other way around.
* Option Binvolves using a large language model (LLM) to extract keywords from the project scope and perform keyword matching on employee profiles. While LLMs can help with keyword extraction, this approach is too simplistic and doesn't leverage advanced retrieval techniques like vector embeddings, which can handle the nuanced and rich semantics of unstructured data. This approach may miss out on subtle but important similarities.
* Option Csuggests calculating a similarity score between each team member's profile and project scope. While this is a good idea, it doesn't specify how to handle the unstructured nature of data efficiently. Iterating through each member's profile individually could be computationally expensive in large teams. It also lacks the mention of using a vector store or an efficient retrieval mechanism.
* Option Dis the correct approach. Here's why:
* Embedding team profiles into a vector store: Using a vector store allows for efficient similarity searches on unstructured data. Embedding the team member profiles into vectors captures their semantics in a way that is far more flexible than keyword-based matching.
* Using project scope for retrieval: Instead of matching keywords, this approach suggests using vector embeddings and similarity search algorithms (e.g., cosine similarity) to find the team members whose profiles most closely align with the project scope.
* Filtering based on availability: Once the best-matched candidates are retrieved based on profile similarity, filtering them by availability ensures that the system provides a practically useful result.
This method efficiently handles large-scale datasets by leveragingvector embeddingsandsimilarity search techniques, both of which are fundamental tools inGenerative AI engineeringfor handling unstructured text.
* Technical References:
* Vector embeddings: In this approach, the unstructured text (employee profiles and project scopes) is converted into high-dimensional vectors using pretrained models (e.g., BERT, Sentence-BERT, or custom embeddings). These embeddings capture the semantic meaning of the text, making it easier to perform similarity-based retrieval.
* Vector stores: Solutions likeFAISSorMilvusallow storing and retrieving large numbers of vector embeddings quickly. This is critical when working with large teams where querying through individual profiles sequentially would be inefficient.
* LLM Integration: Large language models can assist in generating embeddings for both employee profiles and project scopes. They can also assist in fine-tuning similarity measures, ensuring that the retrieval system captures the nuances of the text data.
* Filtering: After retrieving the most similar profiles based on the project scope, filtering based on availability ensures that only team members who are free for the project are considered.
This system is scalable, efficient, and makes use of the latest techniques inGenerative AI, such as vector embeddings and semantic search.
NEW QUESTION # 48
A Generative Al Engineer has successfully ingested unstructured documents and chunked them by document sections. They would like to store the chunks in a Vector Search index. The current format of the dataframe has two columns: (i) original document file name (ii) an array of text chunks for each document.
What is the most performant way to store this dataframe?
- A. First create a unique identifier for each document, then save to a Delta table
- B. Split the data into train and test set, create a unique identifier for each document, then save to a Delta table
- C. Store each chunk as an independent JSON file in Unity Catalog Volume. For each JSON file, the key is the document section name and the value is the array of text chunks for that section
- D. Flatten the dataframe to one chunk per row, create a unique identifier for each row, and save to a Delta table
Answer: D
Explanation:
* Problem Context: The engineer needs an efficient way to store chunks of unstructured documents to facilitate easy retrieval and search. The current dataframe consists of document filenames and associated text chunks.
* Explanation of Options:
* Option A: Splitting into train and test sets is more relevant for model training scenarios and not directly applicable to storage for retrieval in a Vector Search index.
* Option B: Flattening the dataframe such that each row contains a single chunk with a unique identifier is the most performant for storage and retrieval. This structure aligns well with how data is indexed and queried in vector search applications, making it easier to retrieve specific chunks efficiently.
* Option C: Creating a unique identifier for each document only does not address the need to access individual chunks efficiently, which is critical in a Vector Search application.
* Option D: Storing each chunk as an independent JSON file creates unnecessary overhead and complexity in managing and querying large volumes of files.
OptionBis the most efficient and practical approach, allowing for streamlined indexing and retrieval processes in a Delta table environment, fitting the requirements of a Vector Search index.
NEW QUESTION # 49
A Generative Al Engineer has built an LLM-based system that will automatically translate user text between two languages. They now want to benchmark multiple LLM's on this task and pick the best one. They have an evaluation set with known high quality translation examples. They want to evaluate each LLM using the evaluation set with a performant metric.
Which metric should they choose for this evaluation?
- A. BLEU metric
- B. NDCG metric
- C. RECALL metric
- D. ROUGE metric
Answer: A
Explanation:
The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let's evaluate the options.
* Option A: ROUGE metric
* ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It's less suited for translation, where precision and word order matter more.
* Databricks Reference:"ROUGE is commonly used for summarization, not translation evaluation"("Generative AI Cookbook," 2023).
* Option B: BLEU metric
* BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It's widely used, performant, and appropriate for this task.
* Databricks Reference:"BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency"("Building LLM Applications with Databricks").
* Option C: NDCG metric
* NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation.
It's irrelevant for translation evaluation.
* Databricks Reference:"NDCG is suited for ranking tasks, not generative output scoring" ("Databricks Generative AI Engineer Guide").
* Option D: RECALL metric
* Recall measures retrieved relevant items but doesn't evaluate translation quality (e.g., fluency, correctness). It's incomplete for this use case.
* Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.
Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks' guidance on generative tasks.
NEW QUESTION # 50
A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?
- A. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
- B. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
- C. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.
- D. Instruct the LLM to respond with "RAG", "API", or "TABLE" depending on the query, then use text parsing and conditional statements to resolve the query.
Answer: A
Explanation:
In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement atool-based agent system.
Here's how option B works, and why it's the most appropriate answer:
* System Design Using Agent-Based Model:In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can "decide" which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain asystem promptthat informs the LLM about the available tools.
* System Prompt Listing Tools:By creating a well-craftedsystem prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. Theagentwill be responsible for calling the appropriate tool depending on the query.
* Agent Orchestration of Calls:The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.
* Generative AI Tools and Context:This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).
* Why Other Options Are Less Suitable:
* A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn't help with API lookups or table queries.
* C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.
* D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn't scalable. As the standings or events change, the system would need constant updating, making it inefficient.
By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.
NEW QUESTION # 51
A Generative AI Engineer I using the code below to test setting up a vector store:
Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
- A. vsc.create_direct_access_index()
- B. vsc.create_delta_sync_index()
- C. vsc.get_index()
- D. vsc.similarity_search()
Answer: B
Explanation:
Context: The Generative AI Engineer is setting up a vector store using Databricks' VectorSearchClient. This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.
Explanation of Options:
* Option A: vsc.get_index(): This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.
* Option B: vsc.create_delta_sync_index(): After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.
* Option C: vsc.create_direct_access_index(): This function would create an index that directly accesses the data without synchronization. While also a valid approach, it's less likely to be the next logical step if the default setup (typically accommodating changes) is intended.
* Option D: vsc.similarity_search(): This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.
Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, henceOption B.
NEW QUESTION # 52
A Generative Al Engineer is building a production-ready LLM system which replies directly to customers.
The solution makes use of the Foundation Model API via provisioned throughput. They are concerned that the LLM could potentially respond in a toxic or otherwise unsafe way. They also wish to perform this with the least amount of effort.
Which approach will do this?
- A. Host Llama Guard on Foundation Model API and use it to detect unsafe responses
- B. Add a regex expression on inputs and outputs to detect unsafe responses.
- C. Add some LLM calls to their chain to detect unsafe content before returning text
- D. Ask users to report unsafe responses
Answer: A
Explanation:
The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let's assess the options.
* Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses
* Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.
* Databricks Reference:"Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently"("Foundation Model API Documentation," 2023).
* Option B: Add some LLM calls to their chain to detect unsafe content before returning text
* Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.
* Databricks Reference:"Ad-hoc LLM checks are less efficient than purpose-built safety solutions" ("Building LLM Applications with Databricks").
* Option C: Add a regex expression on inputs and outputs to detect unsafe responses
* Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.
* Databricks Reference:"Regex-based filtering is limited for complex safety needs"("Generative AI Cookbook").
* Option D: Ask users to report unsafe responses
* User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn't limit unsafe outputs proactively and requires additional effort for feedback handling.
* Databricks Reference:"Proactive guardrails are preferred over user-driven monitoring" ("Databricks Generative AI Engineer Guide").
Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks' infrastructure for seamless safety integration.
NEW QUESTION # 53
A Generative Al Engineer is ready to deploy an LLM application written using Foundation Model APIs. They want to follow security best practices for production scenarios Which authentication method should they choose?
- A. Use OAuth machine-to-machine authentication
- B. Use a frequently rotated access token belonging to either a workspace user or a service principal
- C. Use an access token belonging to service principals
- D. Use an access token belonging to any workspace user
Answer: C
Explanation:
The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let's evaluate the options based on Databricks' security guidelines for production scenarios.
* Option A: Use an access token belonging to service principals
* Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.
* Databricks Reference:"For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials"("Databricks Security Best Practices,"
2023). Additionally, the "Foundation Model API Documentation" states:"Service principal tokens are recommended for programmatic access to Foundation Model APIs."
* Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal
* Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.
* Databricks Reference:"While token rotation is a good practice, service principals are preferred over user accounts for application authentication"("Managing Tokens in Databricks," 2023).
* Option C: Use OAuth machine-to-machine authentication
* OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks' Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.
* Databricks Reference:"OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads"("Databricks Authentication Guide," 2023).
* Option D: Use an access token belonging to any workspace user
* Using a user's access token ties the application to an individual's identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it's not scalable or auditable for production.
* Databricks Reference:"Avoid using personal user tokens for production applications due to security and governance concerns"("Databricks Security Best Practices," 2023).
Conclusion: Option A is the best choice, as it uses a service principal's access token, aligning with Databricks' security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.
NEW QUESTION # 54
A Generative Al Engineer is building a system which will answer questions on latest stock news articles.
Which will NOT help with ensuring the outputs are relevant to financial news?
- A. Implement a comprehensive guardrail framework that includes policies for content filters tailored to the finance sector.
- B. Increase the compute to improve processing speed of questions to allow greater relevancy analysis C Implement a profanity filter to screen out offensive language
- C. Incorporate manual reviews to correct any problematic outputs prior to sending to the users
Answer: B
Explanation:
In the context of ensuring that outputs are relevant to financial news, increasing compute power (option B) does not directly improve therelevanceof the LLM-generated outputs. Here's why:
* Compute Power and Relevancy:Increasing compute power can help the model process inputs faster, but it does not inherentlyimprove therelevanceof the answers. Relevancy depends on the data sources, the retrieval method, and the filtering mechanisms in place, not on how quickly the model processes the query.
* What Actually Helps with Relevance:Other methods, like content filtering, guardrails, or manual review, can directly impact the relevance of the model's responses by ensuring the model focuses on pertinent financial content. These methods help tailor the LLM's responses to the financial domain and avoid irrelevant or harmful outputs.
* Why Other Options Are More Relevant:
* A (Comprehensive Guardrail Framework): This will ensure that the model avoids generating content that is irrelevant or inappropriate in the finance sector.
* C (Profanity Filter): While not directly related to financial relevancy, ensuring the output is clean and professional is still important in maintaining the quality of responses.
* D (Manual Review): Incorporating human oversight to catch and correct issues with the LLM's output ensures the final answers are aligned with financial content expectations.
Thus, increasing compute power does not help with ensuring the outputs are more relevant to financial news, making option B the correct answer.
NEW QUESTION # 55
A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.
Which action would be most effective in mitigating the problem of offensive text outputs?
- A. Inform the user of the expected RAG behavior
- B. Curate upstream data properly that includes manual review before it is fed into the RAG system
- C. Restrict access to the data sources to a limited number of users
- D. Increase the frequency of upstream data updates
Answer: B
Explanation:
Addressing offensive or inflammatory outputs in a Retrieval-Augmented Generation (RAG) system is critical for improving user experience and ensuring ethical AI deployment. Here's whyDis the most effective approach:
* Manual data curation: The root cause of offensive outputs often comes from the underlying data used to train the model or populate the retrieval system. By manually curating the upstream data and conducting thorough reviews before the data is fed into the RAG system, the engineer can filter out harmful, offensive, or inappropriate content.
* Improving data quality: Curating data ensures the system retrieves and generates responses from a high-quality, well-vetted dataset. This directly impacts the relevance and appropriateness of the outputs from the RAG system, preventing inflammatory content from being included in responses.
* Effectiveness: This strategy directly tackles the problem at its source (the data) rather than just mitigating the consequences (such as informing users or restricting access). It ensures that the system consistently provides non-offensive, relevant information.
Other options, such as increasing the frequency of data updates or informing users about behavior expectations, may not directly mitigate the generation of inflammatory outputs.
NEW QUESTION # 56
After changing the response generating LLM in a RAG pipeline from GPT-4 to a model with a shorter context length that the company self-hosts, the Generative AI Engineer is getting the following error:
What TWO solutions should the Generative AI Engineer implement without changing the response generating model? (Choose two.)
- A. Retrain the response generating model using ALiBi
- B. Reduce the maximum output tokens of the new model
- C. Reduce the number of records retrieved from the vector database
- D. Decrease the chunk size of embedded documents
- E. Use a smaller embedding model to generate
Answer: C,D
Explanation:
* Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.
* Explanation of Options:
* Option A: Use a smaller embedding model to generate- This wouldn't necessarily address the issue of prompt size exceeding the model's token limit.
* Option B: Reduce the maximum output tokens of the new model- This option affects the output length, not the size of the input being too large.
* Option C: Decrease the chunk size of embedded documents- This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.
* Option D: Reduce the number of records retrieved from the vector database- By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.
* Option E: Retrain the response generating model using ALiBi- Retraining the model is contrary to the stipulation not to change the response generating model.
OptionsCandDare the most effective solutions to manage the model's shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.
NEW QUESTION # 57
A Generative Al Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
- A. implementation. Write the Delta table contents to a text column.then embed those texts using an embedding model and store these in the vector index Look up the information based on the embedding as part of the agent logic / tool implementation.
- B. Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
- C. Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation.
- D. Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation.
Answer: D
Explanation:
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance.
Let's evaluate the options.
* Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
* Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent'stool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
* Databricks Reference:"Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems"("Databricks Feature Engineering Guide," 2023).
* Option B: Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
* Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
* Databricks Reference:"Direct SQL queries are flexible but may introduce overhead in real-time applications"("Building LLM Applications with Databricks").
* Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
* Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
* Databricks Reference:"Vector search excels for unstructured data, not structured tabular lookups"("Databricks Vector Search Documentation").
* Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
* Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
* Databricks Reference:"Avoid external systems when Delta tables provide real-time data natively"("Databricks Workflows Guide").
Conclusion: Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
NEW QUESTION # 58
A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?
- A. Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
- B. Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
- C. Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
- D. Compare the Levenshtein distances of returned results against a representative sample of test inputs
Answer: A
Explanation:
The task is to choose between LSH and HNSW for a vector database index, prioritizing semantic accuracy.
The evaluation must assess how well each method retrieves semantically relevant results. Let's evaluate the options.
* Option A: Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
* Cosine similarity measures semantic closeness between vectors, directly assessing retrieval accuracy in a vector database. Comparing returned results' embeddings to test inputs' embeddings evaluates how well LSH or HNSW preserves semantic relationships, aligning with the priority.
* Databricks Reference:"Cosine similarity is a standard metric for evaluating vector search accuracy"("Databricks Vector Search Documentation," 2023).
* Option B: Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
* BLEU evaluates text generation (e.g., translations), not vector retrieval accuracy. It's irrelevant for indexing performance.
* Databricks Reference:"BLEU applies to generative tasks, not retrieval"("Generative AI Cookbook").
* Option C: Compare the Recall-Oriented-Understudy for Gisting Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
* ROUGE is for summarization evaluation, not vector search. It doesn't measure semantic accuracy in retrieval.
* Databricks Reference:"ROUGE is unsuited for vector database evaluation"("Building LLM Applications with Databricks").
* Option D: Compare the Levenshtein distances of returned results against a representative sample of test inputs
* Levenshtein distance measures string edit distance, not semantic similarity in embeddings. It's inappropriate for vector-based retrieval.
* Databricks Reference: No specific support for Levenshtein in vector search contexts.
Conclusion: Option A (cosine similarity) is the correct approach, directly evaluating semantic accuracy in vector retrieval, as recommended by Databricks for Vector Search assessments.
NEW QUESTION # 59
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Ace Databricks Databricks-Generative-AI-Engineer-Associate Certification with Actual Questions Mar 18, 2026 Updated: https://validdumps.free4torrent.com/Databricks-Generative-AI-Engineer-Associate-valid-dumps-torrent.html