Free PDF Quiz 2026 GCP-DE: Perfect Data Engineer Actual Test

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| Section | Weight | Objectives |
|---|
| Building and operationalizing data processing systems | 30% | - Data pipeline implementation
- 1. Dataproc and Spark-based processing
- 2. Dataflow pipeline development
- Data ingestion and transformation
- 1. Pub/Sub streaming ingestion
- 2. ETL/ELT workflows
|
| Maintaining and optimizing data and ML solutions | 20% | - Machine learning integration
- 1. BigQuery ML usage
- 2. Vertex AI integration for pipelines
- Security and governance
- 1. IAM and access control
- 2. Data encryption and compliance
|
| Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
- 1. Cloud Composer workflows
- 2. Scheduling and monitoring pipelines
- Monitoring and troubleshooting
- 1. Logging and observability
- 2. Performance optimization and debugging
|
| Designing data processing systems | 20% | - Data pipeline architecture design
- 1. Batch vs streaming data processing selection
- 2. Scalable data ingestion design
- Storage and data modeling
- 1. Data warehouse design using BigQuery
- 2. Data lake architecture on Google Cloud Storage
|
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Google Data Engineer Sample Questions (Q30-Q35):
NEW QUESTION # 30
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Add ", UNNEST(city)" before the WHERE clause.
- B. Add ", UNNEST(person)" before the WHERE clause.
- C. Change "person" to "city.person".
- D. Change "person" to "person.city".
Answer: B
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu
NEW QUESTION # 31
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?
- A. Cloud Scheduler
- B. Workflow Templates on Cloud Dataproc
- C. Cloud Composer
- D. cron
Answer: B
NEW QUESTION # 32
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
- A. Increase your network bandwidth from Compute Engine to Cloud Storage.
- B. Increase the CPU size on your server.
- C. Increase the size of the Google Persistent Disk on your server.
- D. Increase your network bandwidth from your datacenter to GCP.
Answer: D
NEW QUESTION # 33
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?
- A. Cloud SQL
- B. Cloud Datastore
- C. Cloud Spanner
- D. Cloud Bigtable
Answer: A
NEW QUESTION # 34
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.
- A. once
- B. before
- C. after
- D. only if
Answer: B
Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 35
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