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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality | 20%-25% | - Security and compliance
|
| Topic 2: Designing data processing systems | 22%-27% | - Designing for data ingestion
|
| Topic 3: Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
|
| Topic 4: Managing and optimizing solutions | 20%-25% | - Managing resources and costs
|
Google Data Engineer Sample Questions:
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 GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
- C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
- D. Add a SideInput that returns a Boolean if the element is corrupt.
Correct Answer: C 🗳️
Which Google Cloud Platform service is an alternative to Hadoop with Hive?
- A. Cloud Datastore
- B. Cloud Bigtable
- C. Cloud Dataflow
- D. BigQuery
Correct Answer: D 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Create a cron schedule in Cloud Dataprep.
- B. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
- C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
Correct Answer: D 🗳️
Cloud Dataproc is a managed Apache Hadoop and Apache service.
- A. Blaze
- B. Ignite
- C. Fire
- D. Spark
Correct Answer: D 🗳️
Explanation: Only visible for TestValid members. You can sign-up / login (it's free).
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)
- A. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
- B. Mount the Hive tables locally.
- C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
- D. Mount the Hive tables locally.
- E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
- F. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
- G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
- H. Replicate external Hive tables to the native ones.
- I. Then run the Hadoop utility to copy them do HDF
- J. Mount the Hive tables from HDFS.
- K. Load the ORC files into BigQuer
- L. Replicate external Hive tables to the native ones.
Correct Answer: A,B 🗳️
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