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Free PDF Quiz Google - Professional-Data-Engineer - Google Certified Professional Data Engineer Exam Perfect Vce Free

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Google Professional-Data-Engineer Exam Syllabus Topics:

Topic
Details

Topic 1

Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.

Topic 2

Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.

Topic 3

Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.

Topic 4

Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.

Topic 5

Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.

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Google Professional-Data-Engineer (Google Certified Professional Data Engineer) certification exam is designed to test the skills and knowledge of data professionals who specialize in designing and building data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, developers, architects, and other IT professionals who want to validate their skills and expertise in managing and processing data on Google Cloud Platform.

Google Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems.
Which solutions should you choose?

A. Cloud AutoML Natural Language
B. Cloud Speech-to-Text API
C. Cloud Natural Language API
D. Dialogflow Enterprise Edition

Answer: A

NEW QUESTION # 82
You are part of a healthcare organization where data is organized and managed by respective data owners in various storage services. As a result of this decentralized ecosystem, discovering and managing data has become difficult You need to quickly identify and implement a cost-optimized solution to assist your organization with the following
* Data management and discovery
* Data lineage tracking
* Data quality validation
How should you build the solution?

A. Use BigLake to convert the current solution into a data lake architecture.
B. Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation.
C. Use Dataplex to manage data, track data lineage, and perform data quality validation.
D. Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.

Answer: C

Explanation:
Dataplex is a Google Cloud service that provides a unified data fabric for data lakes and data warehouses. It enables data governance, management, and discovery across multiple data domains, zones, and assets. Dataplex also supports data lineage tracking, which shows the origin and transformation of data over time. Dataplex also integrates with Dataprep, a data preparation and quality tool that allows users to clean, enrich, and transform data using a visual interface. Dataprep can also monitor data quality and detect anomalies using machine learning. Therefore, Dataplex is the most suitable solution for the given scenario, as it meets all the requirements of data management and discovery, data lineage tracking, and data quality validation. Reference:
Dataplex overview
Automate data governance, extend your data fabric with Dataplex-BigLake integration Dataprep documentation

NEW QUESTION # 83
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. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
B. Manually start the Cloud Dataflow job each morning when you get into the office.
C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
D. Change the processing job to use Google Cloud Dataproc instead.

Answer: C

NEW QUESTION # 84
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc?
(Choose two.)

A. Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.
B. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables.
Replicate external Hive tables to the native ones.
C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

Answer: D,E

Explanation:
HDFS lies on datanode, data on masternode needs to be copied on datanode.

NEW QUESTION # 85
To give a user read permission for only the first three columns of a table, which access control method would you use?

A. Predefined role
B. It's not possible to give access to only the first three columns of a table.
C. Authorized view
D. Primitive role

Answer: C

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.

NEW QUESTION # 86
......

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