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Google Data Engineer : GCP-DE
- Exam Code: GCP-DE
- Exam Name: Data Engineer
- Updated: Oct 12, 2026
- Q & A: 77 Questions and Answers
About Google Data Engineer : GCP-DE Exam
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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 24% | - Designing data pipelines
|
| Maintaining and automating data workloads | 18% | - Operationalizing workloads
|
| Ingesting and processing data | 25% | - Building data pipelines
|
| Preparing data for analysis and machine learning | 13% | - Enabling data analysis
|
| Storing and managing data | 20% | - Implementing storage solutions
|
Google Data Engineer Sample Questions:
Question #1
What are all of the BigQuery operations that Google charges for?
- A. Storage, queries, and loading data from a file
- B. Queries and streaming inserts
- C. Storage, queries, and exporting data
- D. Storage, queries, and streaming inserts
Correct Answer: D 🗳️
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Question #2
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?
- A. Eliminate features that are highly correlated to the output labels.
- B. Remove the features that have null values for more than 50% of the training records.
- C. Instead of feeding in each feature individually, average their values in batches of 3.
- D. Combine highly co-dependent features into one representative feature.
Correct Answer: D 🗳️
Question #3
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
- A. Python using MapReduce
- B. HiveQL using Hive
- C. PigLatin using Pig
- D. Java using MapReduce
Correct Answer: A 🗳️
Question #4
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?
- A. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
- B. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
- C. Partition the tables by a column containing a TIMESTAMP or DATE Type.
- D. Re-create the tables using DD
- E. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
- F. Write an Apache Beam pipeline that creates a BigQuery table per data
Correct Answer: E 🗳️
Question #5
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
- A. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- C. Use Cloud Deployment Manager to automate access provision.
- D. Introduce resource hierarchy to leverage access control policy inheritance.
- E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- F. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
Correct Answer: D,E 🗳️
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