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Exam Professional-Data-Engineer Study Guide - Professional-Data-Engineer Accurate Test

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Google Professional-Data-Engineer exam is a certification exam offered by Google for professionals who work with data engineering. Professional-Data-Engineer exam is designed to test the individual's knowledge and skills in using Google Cloud Platform tools and services for data engineering. Professional-Data-Engineer exam is intended to validate the candidate's ability to design, build, operationalize, and secure data processing systems using Google Cloud Platform technologies.

Google Professional-Data-Engineer Certification Exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

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Google Certified Professional Data Engineer Exam Sample Questions (Q315-Q320):

NEW QUESTION # 315
Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

A. Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.
B. Use dynamic data masking and revoke viewer permissions when employees leave the organization.
C. Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.
D. Use AEAD functions and delete keys when employees leave the organization.

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
The core requirement is to make specific data values permanently unreadable, a concept often referred to as cryptographic erasure or "crypto-shredding." Option C is the correct answer because it achieves this at a granular, field-level. BigQuery's AEAD (Authenticated Encryption with Associated Data) functions allow you to encrypt individual field values within your tables using a keyset from Cloud KMS. You can encrypt the sensitive data, store the encrypted value in BigQuery, and when an employee leaves, you simply destroy the specific KMS key version used for their data. Once the key is destroyed, the encrypted data is permanently unrecoverable, thus rendering it unreadable.
Option A and B are incorrect because data masking and column-level access controls are about controlling access to the data, not about making the underlying data itself unreadable. If an administrator with sufficient privileges were to query the table, the data would still be there in its original form. Revoking permissions prevents a specific user from seeing the data, but it doesn't erase it.
Option D is incorrect because Customer-Managed Encryption Keys (CMEK) operate at the entire table level.
Deleting a CMEK key would make the entire table unreadable, which is far too broad and disruptive. The goal is to target specific sensitive values, not destroy whole tables.
Reference (Google Cloud Documentation Concepts):
This solution leverages field-level encryption within BigQuery. The official documentation for "Protecting data with Cloud KMS keys" states that BigQuery's AEAD functions (AEAD.ENCRYPT and AEAD.
DECRYPT) are the intended mechanism for this purpose. This approach provides fine-grained control, allowing you to manage the lifecycle of encrypted data by managing the lifecycle of the encryption keys in Cloud Key Management Service (Cloud KMS). The act of destroying a key version is the mechanism for crypto-shredding.

NEW QUESTION # 316
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

A. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
B. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
C. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
D. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Answer: C

NEW QUESTION # 317
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

A. BETWEEN
B. WHERE
C. LIMIT
D. SELECT

Answer: D

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference: https://cloud.google.com/bigquery/launch-
checklist#architecture_design_and_development_checklist

NEW QUESTION # 318
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

A. BigQuery cannot be accessed from local machines
B. Your gcloud does not have access to the BigQuery resources
C. You are missing gcloud on your machine
D. Pipelines cannot be run locally

Answer: B

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink

NEW QUESTION # 319
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance.
You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

A. Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.
B. Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance
C. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.
D. Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

Answer: C

Explanation:
The import and export feature uses the native RDB snapshot feature of Redis to import data into or export data out of a Memorystore for Redis instance. The use of the native RDB format prevents lock-in and makes it very easy to move data within Google Cloud or outside of Google Cloud. Import and export uses Cloud Storage buckets to store RDB files. Reference:
https://cloud.google.com/memorystore/docs/redis/import-export-overview

NEW QUESTION # 320
......

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