PRACTICE PROFESSIONAL-DATA-ENGINEER EXAMS & PROFESSIONAL-DATA-ENGINEER EXAM TOPICS

Practice Professional-Data-Engineer Exams & Professional-Data-Engineer Exam Topics

Practice Professional-Data-Engineer Exams & Professional-Data-Engineer Exam Topics

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Tags: Practice Professional-Data-Engineer Exams, Professional-Data-Engineer Exam Topics, Cert Professional-Data-Engineer Exam, New Professional-Data-Engineer Exam Answers, Professional-Data-Engineer Paper

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The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing systems design, data modeling, data ingestion, data transformation, data storage, data analysis, and machine learning. You will be tested on your ability to design and implement data processing systems using Google Cloud technologies such as BigQuery, Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub. Professional-Data-Engineer exam also covers best practices for data security and compliance, as well as troubleshooting and optimization techniques. Passing Professional-Data-Engineer exam requires a strong understanding of cloud computing principles and a solid grasp of data engineering concepts, making it a challenging but rewarding certification to earn.

Google Professional-Data-Engineer Certification Exam is a rigorous exam that requires a significant amount of preparation. Candidates must have extensive experience working with big data solutions and be familiar with the latest trends in data processing and analysis. Google Certified Professional Data Engineer Exam certification is highly valued in the industry and can lead to new career opportunities and higher salaries.

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Professional-Data-Engineer Exam Topics & Cert Professional-Data-Engineer Exam

By sitting in these scenarios, you will be able to kill test anxiety. As a result, you will take the final Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) exam with no fear. The web-based Professional-Data-Engineer practice exam software not only works on Windows but also on Linux, iOS, Mac, and Android. Furthermore, this online software of the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) practice test is compatible with Internet Explorer, MS Edge, Chrome, Firefox, Safari, and Opera.

Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

Google Certified Professional Data Engineer Exam Sample Questions (Q293-Q298):

NEW QUESTION # 293
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

  • A. Cloud Dataflow
  • B. Cloud Datastore
  • C. BigQuery
  • D. Cloud Bigtable

Answer: C

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis.
Google BigQuery is an enterprise data warehouse.


NEW QUESTION # 294
An organization maintains a Google BigQuery dataset that contains tables with user-level dat A.
They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

  • A. Create and share a new dataset and view that provides the aggregate results.
  • B. Create and share an authorized view that provides the aggregate results.
  • C. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
  • D. Create and share a new dataset and table that contains the aggregate results.

Answer: C


NEW QUESTION # 295
You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?

  • A. Use a service account with the ability to read the batch files and to write to BigQuery
  • B. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
  • C. Grant the Project Owner role to a service account, and run the job with it
  • D. Restrict the Google Cloud Storage bucket so only you can see the files

Answer: C


NEW QUESTION # 296
You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API Following Google's best practices, you have both a staging and a production table for the data How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?

  • A. Have a staging table that moves the staged data over to the production table and deletes the contents of the
    staging table every three hours
  • B. Have a staging table that is an append-only model, and then update the production table every three hours
    with the changes written to staging
  • C. Have a staging table that is an append-only model, and then update the production table every ninety
    minutes with the changes written to staging
  • D. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes

Answer: D


NEW QUESTION # 297
Your company is implementing a data warehouse using BigQuery and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

  • A. Shard the data by customer ID
  • B. Materialize the dimensional data in views
  • C. Partition the data by transaction date
  • D. Denormalize the data

Answer: C


NEW QUESTION # 298
......

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