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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 20% | - Storage and data modeling
|
| Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
|
| Maintaining and optimizing data and ML solutions | 20% | - Security and governance
|
| Building and operationalizing data processing systems | 30% | - Data ingestion and transformation
|
Google Data Engineer Sample Questions:
1. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
A) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
C) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
D) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
E) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
2. When a Cloud Bigtable node fails, is lost.
A) all data
B) the last transaction
C) the time dimension
D) no data
3. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
A) VPN
B) SOCKS
C) HTTP
D) HTTPS
4. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
A) An hourly watermark
B) The with Allowed Lateness method
C) An event time trigger
D) A processing time trigger
5. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
A) Create a table on Cloud Spanner, and insert and delete rows with the job information
B) Create an API using App Engine to receive and send messages to the applications
C) Create a table on Cloud SQL, and insert and delete rows with the job information
D) Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: D |
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