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Google Professional-Data-Engineer certification is aimed at data engineers who have experience working with large-scale data processing systems. Candidates who pass the exam will have demonstrated their ability to design and implement highly scalable data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is highly regarded by employers in the industry and is a valuable asset for data engineers looking to advance their careers.

Google Professional-Data-Engineer Certification Exam is intended for data engineers, data scientists, and other IT professionals who want to demonstrate their proficiency in Google Cloud Platform data engineering technologies. Professional-Data-Engineer exam covers topics such as design and implementation of data processing systems, building and maintaining databases, data analysis and visualization, and machine learning implementation.

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Google Certified Professional Data Engineer Exam Sample Questions (Q217-Q222):

NEW QUESTION # 217
You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?

A. Schedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
B. Create a streaming Dataflow job that reads continually from the Pub/Sub topic and performs aggregations using tumbling windows.
C. Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.
D. Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.

Answer: B

Explanation:
A tumbling window represents a consistent, disjoint time interval in the data stream.
Reference:
https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines#tumbling-windows

NEW QUESTION # 218
What are all of the BigQuery operations that Google charges for?

A. Queries and streaming inserts
B. Storage, queries, and exporting data
C. Storage, queries, and loading data from a file
D. Storage, queries, and streaming inserts

Answer: D

Explanation:
Explanation
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 219
You are on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?

A. Create the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the created Cloud HSM key while creating the BigQuery resources.
B. Create the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the created Cloud KMS key while creating the BigQuery resources.
C. Create the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.
D. Create the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created Cloud KMS key while creating the BigQuery resources.

Answer: D

NEW QUESTION # 220
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

A. SELECT
B. WHERE
C. LIMIT
D. BETWEEN

Answer: A

Explanation:
SELECT allows you to query specific columns rather than the whole table. LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference:
https://cloud.google.com/bigquery/launch-checklist#architecture_design_and_development_checklist

NEW QUESTION # 221
You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

A. Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
B. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage
C. Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.
D. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage

Answer: D

NEW QUESTION # 222
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