Free Access Google.Professional-Data-Engineer.v2022-02-11.q268 Practice Test (Page 29)

Question 136

When you design a Google Cloud Bigtable schema it is recommended that you _________.

A.Avoid schema designs that are based on NoSQL concepts

B.Create schema designs that are based on a relational database design

C.Avoid schema designs that require atomicity across rows

D.Create schema designs that require atomicity across rows

Question 137

You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

A.Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

B.Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery

C.Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery

D.Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.

Question 138

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs
and their timings during their campaign. Engineers have decided to use windowing and transformation in
Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud
Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A.They have not set the triggers to accommodate the data coming in late, which causes the job to fail

B.They have not applied a global windowing function, which causes the job to fail when the pipeline is
created

C.They have not applied a non-global windowing function, which causes the job to fail when the pipeline
is created

D.They have not assigned the timestamp, which causes the job to fail

Question 139

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

A.BETWEEN

B.WHERE

C.SELECT

D.LIMIT

Question 140

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A.Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.

B.Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

C.Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

D.Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

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