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Professional-Data-Engineer題庫資訊 - Professional-Data-Engineer考試大綱

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最新的 Google Cloud Certified Professional-Data-Engineer 免費考試真題 (Q242-Q247):

問題 #242
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

A. dataflow.viewer
B. dataflow.compute
C. dataflow.developer
D. dataflow.worker

答案：D

解題說明：
The dataflow.worker role provides the permissions necessary for a Compute Engine service account to execute work units for a Dataflow pipeline

問題 #243
You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

A. Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
B. Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.
C. Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
D. Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word "street" appears.

答案：A

解題說明：
To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here's why option A is the best choice:
* Cloud Data Loss Prevention (DLP):
* Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre- defined infoTypes for various kinds of sensitive data, including street addresses.
* Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.
* Deep Inspection Job:
* A deep inspection job allows you to scan entire tables for sensitive information.
* By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.
* Scalability and Accuracy:
* Cloud DLP is scalable and can handle large datasets efficiently.
* It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.
Steps to Implement:
* Set Up Cloud DLP:
* Enable the Cloud DLP API in your Google Cloud project.
* Create an Inspection Template:
* Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.
* Run Deep Inspection Jobs:
* Create and run a deep inspection job for each table in your dataset using the inspection template.
* Review the inspection job results to retrieve all occurrences of street addresses.
Reference Links:
* Cloud DLP Documentation
* Creating Inspection Jobs

問題 #244
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

A. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
B. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
C. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
D. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

答案：B

問題 #245
What are two of the benefits of using denormalized data structures in BigQuery?

A. Increases query speed, makes queries simpler
B. Reduces the amount of data processed, reduces the amount of storage required
C. Reduces the amount of data processed, increases query speed
D. Reduces the amount of storage required, increases query speed

答案：A

解題說明：
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table.
Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

問題 #246
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
* No interaction by the user on the site for 1 hour
* Has added more than $30 worth of products to the basket
* Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

A. Use a fixed-time window with a duration of 60 minutes.
B. Use a sliding time window with a duration of 60 minutes.
C. Use a session window with a gap time duration of 60 minutes.
D. Use a global window with a time based trigger with a delay of 60 minutes.

答案：C

問題 #247
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此外，這些Testpdf Professional-Data-Engineer考試題庫的部分內容現在是免費的：https://drive.google.com/open?id=1Xt7iQCWI8k7Kjm0WoSYWlpqfUpDD-F4m