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Free PDF Quiz 2025 Professional-Data-Engineer: Newest Google Certified Professional Data Engineer Exam Valid Braindumps Ppt

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Google Certified Professional Data Engineer Exam Sample Questions (Q240-Q245):

NEW QUESTION # 240
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

A. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
D. Create a table in BigQuery, and append the new samples for CPU and memory to the table

Answer: C

NEW QUESTION # 241
Your startup has a web application that currently serves customers out of a single region in Asi a. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A. Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.
B. Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.
C. Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.
D. Use a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US. Europe, and Asiareplication alter securing funding

Answer: C

Explanation:
https://cloud.google.com/spanner/docs/instance-configurations#tradeoffs_regional_versus_multi-region_configurations

NEW QUESTION # 242
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

A. Cloud BigTable
B. Cloud SQL
C. BigQuery
D. Cloud Datastore

Answer: A

Explanation:
ference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION # 243
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street
Tim,553 Y street
Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A. Sink API
B. Source API
C. ParDo
D. Data extraction

Answer: C

Explanation:
Explanation
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 244
You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

A. Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
B. Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
C. Load files from AWS and Azure to Cloud Storage with Cloud Shell gautil rsync arguments.
D. Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Answer: D

Explanation:
BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance. References:
* 1: Introduction to BigLake tables
* 2: Deep dive on how BigLake accelerates query performance
* 3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)

NEW QUESTION # 245
......

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