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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question #1
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 event time trigger
B. An hourly watermark
C. A processing time trigger
D. The with Allowed Lateness method

Question #2
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. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
E. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Question #3
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
C. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
D. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination

Question #4
Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Spark
B. Fire
C. Ignite
D. Blaze

Question #5
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
B. Create an application that writes to a Cloud SQL database to store the dat
C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
E. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
F. Create an application that provides an AP

Solutions: