QUIZ MARVELOUS PROFESSIONAL-DATA-ENGINEER - CERTIFICATION GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM EXAM INFOR

Quiz Marvelous Professional-Data-Engineer - Certification Google Certified Professional Data Engineer Exam Exam Infor

Quiz Marvelous Professional-Data-Engineer - Certification Google Certified Professional Data Engineer Exam Exam Infor

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

Google Professional-Data-Engineer exam is a certification offered by Google to those who want to showcase their expertise in designing, building, and maintaining data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is designed for professionals who have a deep understanding of data engineering and can leverage Google Cloud technologies to create scalable and efficient data pipelines. Professional-Data-Engineer Exam assesses the candidate's ability to design data processing systems, build and operationalize data pipelines, and manage and monitor data processing infrastructure.

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Google Certified Professional Data Engineer Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
You need to choose a database for a new project that has the following requirements:
* Fully managed
* Able to automatically scale up
* Transactionally consistent
* Able to scale up to 6 TB
* Able to be queried using SQL
Which database do you choose?

  • A. Cloud Bigtable
  • B. Cloud SQL
  • C. Cloud Spanner
  • D. Cloud Datastore

Answer: C


NEW QUESTION # 55
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?

  • A. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
  • B. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
  • C. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
  • D. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

Answer: A


NEW QUESTION # 56
What are all of the BigQuery operations that Google charges for?

  • A. Storage, queries, and exporting data
  • B. Queries and streaming inserts
  • C. Storage, queries, and streaming inserts
  • D. Storage, queries, and loading data from a file

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.


NEW QUESTION # 57
You work for a manufacturing plant that batches application log files together into a single log file once a
day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make
sure the log file in processed once per day as inexpensively as possible. What should you do?

  • A. Change the processing job to use Google Cloud Dataproc instead.
  • B. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
  • C. Manually start the Cloud Dataflow job each morning when you get into the office.
  • D. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Answer: B


NEW QUESTION # 58
You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely.
The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data What should you do?
Choose 2 answers

  • A. Replace the authorized dataset with an authorized view Use row-level security and apply filter_ expression to limit data access.
  • B. Enforce access control in the policy tag taxonomy.
  • C. Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.
  • D. Remove the bigquery. dataViewer role from the data analytics team on the authorized datasets.
  • E. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.

Answer: B,E

Explanation:
To ensure that the data analytics team does not have access to sensitive columns, you should:
* B. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags. This role allows users to read metadata for data assets that have policy tags applied, which could include sensitive information.
* C. Enforce access control in the policy tag taxonomy. By setting access control at the policy tag level, you can restrict access to specific columns within a dataset, ensuring that only authorized users can view sensitive data.


NEW QUESTION # 59
......

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