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Google Updated Professional-Data-Engineer Exam Questions and Answers by elowen

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Google Professional-Data-Engineer Exam Overview :

Exam Name: Google Professional Data Engineer Exam
Exam Code: Professional-Data-Engineer Dumps
Vendor: Google Certification: Google Cloud Certified
Questions: 400 Q&A's Shared By: elowen
Question 28

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?

Options:

A.

Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

B.

Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

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.

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.

Discussion
Question 29

You monitor and optimize the BigQuery instance for your team. You notice that a particular daily report that uses a large JOIN operation is consistently slow. You want to examine the query's execution plan to identify potential performance bottlenecks within the JOIN as quickly as possible. What should you do?

Options:

A.

Review the BigQuery audit logs in Cloud Logging.

B.

Run a query on the INFORMATION_SCHEMA.JOBS_BY_PROJECT view filtering by the job_id and analyze total_bytes_processed.

C.

Leverage BigQuery's Query History view and analyze the execution graph.

D.

Use the bq query --dry_run command to review the estimated number of bytes read and review query syntax.

Discussion
Question 30

Your company wants to implement a Retrieval-Augmented Generation (RAG) system to allow employees to query an extensive knowledge base of internal documents, such as policy manuals and project reports. You need to prepare this unstructured text for embedding to be used in the RAG system. What should you do to ensure the system can retrieve the most relevant information?

Options:

A.

Convert the unstructured documents into high-dimensional numerical vectors that capture the semantic meaning and relationships of the text.

B.

Store the documents as compressed files in a traditional relational database to enable more efficient storage and retrieval.

C.

Use Cloud Data Loss Prevention (Cloud DLP) to scan and redact sensitive information within the documents before processing.

D.

Index each word from the documents into a search engine to enable keyword-based search.

Discussion
Question 31

You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

Options:

A.

Schedule a daily BigQuery snapshot of the table.

B.

Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.

C.

Schedule a daily copy of the dataset to a backup region.

D.

Modify ETL job to load the data into both the current and another backup region.

Discussion
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