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| Section | Weight | Objectives |
|---|---|---|
| AI Software Architectures | 18% | - Scaling and Orchestration
|
| AI Lifecycle | 27% | - Data Preparation
|
| AI Common Challenges | 22% | - Resource Management
|
| AI Hardware Architectures | 18% | - Networking and Storage
|
| AI Overview | 15% | - Algorithm Types
|
1. The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?
A) The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
B) The PersistentVolumeClaim for the pod is in a 'Pending' state.
C) The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
D) The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
2. An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)
A) Use a RAG architecture for the sales forecasting model.
B) Use NetApp StorageGRID as the primary storage for the high-performance training stage.
C) Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
D) Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
E) Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
F) Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
3. A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?
A) The server's network interface is saturated.
B) The storage system (ASA) is unable to provide sufficient read throughput.
C) The server's CPU is overloaded.
D) The application is experiencing excessive memory paging.
4. A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?
A) Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
B) Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
C) Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
D) Use a single, large NetApp ASA system for both the object data lake and the vector database.
5. A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?
A) NetApp FlexClone
B) NetApp FabricPool
C) NetApp SnapMirror
D) NetApp XCP
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C,D,E | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: A |
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