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To prepare for the Google Professional-Data-Engineer certification exam, candidates must have a solid understanding of data engineering fundamentals, as well as knowledge of the Google Cloud Platform and its associated services. Candidates can use a variety of study resources, such as official Google Cloud Platform documentation, online courses, and practice exams, to prepare for the exam. Additionally, candidates should have experience working with data processing systems, data warehousing, and data analysis tools.

Google Professional Data Engineer exam covers a wide range of topics, including the understanding of the Google Cloud Platform for storing, processing, and analyzing data, designing data processing systems, data modeling, data security, and compliance. Additionally, the exam tests the candidate's knowledge of implementing data pipelines, data transformation and processing, and machine learning models on the Google Cloud Platform. Passing Professional-Data-Engineer Exam demonstrates that the candidate has the skills and knowledge required to design and build data processing systems that meet business requirements and scale efficiently on the Google Cloud Platform.

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Google Certified Professional Data Engineer Exam Sample Questions (Q161-Q166):

NEW QUESTION # 161
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

A. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull
B. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
C. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
D. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Answer: C

Explanation:
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

NEW QUESTION # 162
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 100 MB of data
B. Single, Global Window
C. Windows at every 10 minutes
D. Windows at every 1 minute

Answer: B

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections

NEW QUESTION # 163
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

A. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
C. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
D. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

Answer: C

Explanation:
References:

NEW QUESTION # 164
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?

A. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
B. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
C. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
D. Create groups for your users and give those groups access to the dataset

Answer: B

NEW QUESTION # 165
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

A. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.
B. Select random samples from the tables using the RAND() function and compare the samples.
C. Select random samples from the tables using the HASH() function and compare the samples.
D. Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Answer: A

Explanation:
Full comparison with this option, rest are comparison on sample which doesn't ensure all the data will be ok.

NEW QUESTION # 166
......

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