New Launch GCP-DE Dumps [2026] - Google GCP-DE Exam Questions

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| Section | Weight | Objectives |
|---|
| Ensuring solution quality | 20%-25% | - Security and compliance
- 1. Protect sensitive data
- 2. Implement encryption and compliance controls
- 3. Manage IAM and access control
- Data quality management
- 1. Validate and clean data
- 2. Maintain metadata and lineage
- 3. Implement data governance
|
| Managing and optimizing solutions | 20%-25% | - Reliability and scalability
- 1. Design highly available systems
- 2. Scale data workloads efficiently
- 3. Implement disaster recovery strategies
- Managing resources and costs
- 1. Control operational costs
- 2. Tune query and pipeline performance
- 3. Optimize storage and compute usage
|
| Designing data processing systems | 22%-27% | - Designing data pipelines
- 1. Optimize pipeline performance and cost
- 2. Build ETL and ELT pipelines
- 3. Design orchestration workflows
- Designing for data ingestion
- 1. Select appropriate storage systems
- 2. Design scalable and reliable ingestion architecture
- 3. Design batch and streaming ingestion solutions
|
| Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
- 1. Implement batch processing systems
- 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- 3. Implement streaming data systems
- Operationalizing systems
- 1. Manage logging and alerting
- 2. Implement fault tolerance and recovery
- 3. Monitor data pipelines and workloads
|
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Google Data Engineer Sample Questions (Q70-Q75):
NEW QUESTION # 70
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 Composer
- B. Cloud Scheduler
- C. Workflow Templates on Cloud Dataproc
- D. cron
Answer: C
NEW QUESTION # 71
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
- A. Before you test, run a heavy pre-test for several minutes.
- B. Use at least 300 GB of data.
- C. Run your test for at least 10 minutes.
- D. Do not use a production instance.
Answer: D
Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance
NEW QUESTION # 72
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. If that number falls below 4000, send an alert.
- B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- C. Set a sliding time window of 1 hour every 5 minute
- D. If that number falls below 4000, send an alert.
- E. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
- F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- G. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
- I. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
- J. Consume the stream of data in Cloud Dataflow using Kafka I
- K. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
- L. Consume the stream of data in Cloud Dataflow using Kafka I
- M. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
Answer: G
NEW QUESTION # 73
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
- A. Add a SideInput that returns a Boolean if the element is corrupt.
- B. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
- C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
Answer: B
NEW QUESTION # 74
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 the CPU size on your server.
- B. Increase your network bandwidth from Compute Engine to Cloud Storage.
- 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 # 75
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