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Google Certified Professional Data Engineer Exam Sample Questions (Q229-Q234):

NEW QUESTION # 229
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

• A. Use .fromQuery operation to read specific fields from the table.
• B. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
• C. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
• D. Specify the TableReference object in the code.

Answer: C
NEW QUESTION # 230
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by datereleased or all movies with tag=Comedy ordered by datereleased. How should you avoid a combinatorial explosion in the number of indexes?

• A. Option B.
• B. Option D
• C. Option C
• D. Option A

Answer: D
NEW QUESTION # 231
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 an authorized view that provides the aggregate results.
• B. Create and share a new dataset and table that contains 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 view that provides the aggregate results.

Answer: C
NEW QUESTION # 232
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

• A. Recreate the table with a partitioning column and clustering column.
• B. Use the LIMIT keyword to reduce the number of rows returned.
• C. Create a separate table for each ID.
• D. Use the bq query - -maximumbytesbilled flag to restrict the number of bytes billed.

Answer: A Explanation:
https://cloud.google.com/bigquery/docs/best-practices-costs
NEW QUESTION # 233
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

• A. type
• B. node
• C. label
• D. zone

Answer: D Explanation:
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.
Reference: https://cloud.google.com/dataproc/docs/tutorials/python-library- example#createanewclouddataproc_cluste
NEW QUESTION # 234
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