RELIABLE GOOGLE PROFESSIONAL-DATA-ENGINEER EXAM BRAINDUMPS, PROFESSIONAL-DATA-ENGINEER VALID TEST BRAINDUMPS

Reliable Google Professional-Data-Engineer Exam Braindumps, Professional-Data-Engineer Valid Test Braindumps

Reliable Google Professional-Data-Engineer Exam Braindumps, Professional-Data-Engineer Valid Test Braindumps

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Google Certified Professional Data Engineer Exam Sample Questions (Q287-Q292):

NEW QUESTION # 287
Which Java SDK class can you use to run your Dataflow programs locally?

  • A. MachineRunner
  • B. DirectPipelineRunner
  • C. LocalPipelineRunner
  • D. LocalRunner

Answer: B

Explanation:
Explanation
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun


NEW QUESTION # 288
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

  • A. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
  • B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.
  • C. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
  • D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.

Answer: A


NEW QUESTION # 289
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 # 290
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, increases query speed
  • C. Reduces the amount of storage required, increases query speed
  • D. Reduces the amount of data processed, reduces the amount of storage required

Answer: A

Explanation:
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.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


NEW QUESTION # 291
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

  • A. Re-create the table using data partitioning on the package delivery date.
  • B. Implement clustering in BigQuery on the package-tracking ID column.
  • C. Tier older data onto Cloud Storage files, and leverage extended tables.
  • D. Implement clustering in BigQuery on the ingest date column.

Answer: D


NEW QUESTION # 292
......

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