Google GCP-DE Q&A - in .pdf

  • GCP-DE pdf
  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Aug 08, 2026
  • Q & A: 77 Questions and Answers
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  • Updated: Aug 08, 2026
  • Q & A: 77 Questions and Answers
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Google GCP-DE Q&A - Testing Engine

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  • Exam Code: GCP-DE
  • Exam Name: Data Engineer
  • Updated: Aug 08, 2026
  • Q & A: 77 Questions and Answers
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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems24%- Designing data pipelines
  • 1. Planning for data migration and integration
  • 2. Designing for data transformation and enrichment
  • 3. Defining architecture for batch and streaming processing
- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability, scalability, and efficiency
Topic 2: Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Orchestrating and scheduling jobs
  • 2. Monitoring and troubleshooting pipelines
  • 3. Ensuring reliability and recoverability
- Automation and optimization
  • 1. Optimizing resource usage and costs
  • 2. Automating deployment and management
Topic 3: Preparing data for analysis and machine learning13%- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Implementing data sharing and governance
  • 3. Preparing datasets for querying and reporting
Topic 4: Storing and managing data20%- Implementing storage solutions
  • 1. Managing data lifecycle and retention
  • 2. Designing data warehouses and data lakes
  • 3. Using data storage services appropriately
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Topic 5: Ingesting and processing data25%- Building data pipelines
  • 1. Handling data quality and consistency
  • 2. Developing batch and streaming workflows
  • 3. Implementing ingestion mechanisms
- Transforming data
  • 1. Managing schema evolution
  • 2. Optimizing transformations for performance
  • 3. Applying data processing logic

Google Data Engineer Sample Questions:

1. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
B) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
C) Denormalize the data as must as possible.
D) Preserve the structure of the data as much as possible.
E) Use BigQuery'ssupport for external data sources to query.
F) Use BigQuery UPDATE to further reduce the size of the dataset.


2. Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
B) Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
C) Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D) Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric


3. Which of these sources can you not load data into BigQuery from?

A) Google Drive
B) Google Cloud Storage
C) Google Cloud SQL
D) File upload


4. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) HTTP
B) VPN
C) HTTPS
D) SOCKS


5. Which of the following statements is NOT true regarding Bigtable access roles?

A) You can configure access control only at the project level.
B) To give a user access to only one table in a project, you must configure access through your application.
C) Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
D) To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.


Solutions:

Question # 1
Answer: A,B
Question # 2
Answer: C
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: D

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