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Google Certified Professional Data Engineer Exam Sample Questions (Q234-Q239):

NEW QUESTION # 234
Which Java SDK class can you use to run your Dataflow programs locally?

  • A. MachineRunner
  • B. LocalPipelineRunner
  • C. LocalRunner
  • D. DirectPipelineRunner

Answer: D

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference: https://cloud.google.com/dataflow/java-
sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRunner


NEW QUESTION # 235
Which of these is NOT a way to customize the software on Dataproc cluster instances?

  • A. Modify configuration files using cluster properties
  • B. Configure the cluster using Cloud Deployment Manager
  • C. Set initialization actions
  • D. Log into the master node and make changes from there

Answer: B

Explanation:
Explanation
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.
When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties


NEW QUESTION # 236
When a Cloud Bigtable node fails, ____ is lost.

  • A. no data
  • B. the time dimension
  • C. all data
  • D. the last transaction

Answer: A

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node. Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node. Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost
Reference: https://cloud.google.com/bigtable/docs/overview


NEW QUESTION # 237
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

  • A. YARN
  • B. Pig
  • C. Hive
  • D. Spark

Answer: B,C,D

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.


NEW QUESTION # 238
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

  • A. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.
  • B. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
  • C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
  • D. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.

Answer: A

Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
Reference:
Connected Sheets | Google Sheets | Google for Developers
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs


NEW QUESTION # 239
......

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