Professional-Data-Engineer Google Exam Questions and Free Practice Test

Question 19

- (Exam Topic 5)
What are two of the benefits of using denormalized data structures in BigQuery?

A. Reduces the amount of data processed, reduces the amount of storage required

B. Increases query speed, makes queries simpler

C. Reduces the amount of storage required, increases query speed

D. Reduces the amount of data processed, increases query speed

Correct Answer:B
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

Question 20

- (Exam Topic 6)
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 Dataprep to find null values in sample source dat

B. Convert all nulls to ‘none’ using a Cloud Dataproc job.

C. Use Cloud Dataprep to find null values in sample source dat

D. Convert all nulls to 0 using a Cloud Dataprep job.

E. Use Cloud Dataflow to find null values in sample source dat

F. Convert all nulls to ‘none’ using a Cloud Dataprep job.

G. Use Cloud Dataflow to find null values in sample source dat

H. Convert all nulls to using a custom script.

Correct Answer:C

Question 21

- (Exam Topic 6)
You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API Following Google's best practices, you have both a staging and a production table for the data How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?

A. Have a staging table that is an append-only model, and then update the production table every three hours with the changes written to staging

B. Have a staging table that is an append-only model, and then update the production table every ninety minutes with the changes written to staging

C. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every three hours

D. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes

Correct Answer:D

Question 22

- (Exam Topic 5)
Which Java SDK class can you use to run your Dataflow programs locally?

A. LocalRunner

B. DirectPipelineRunner

C. MachineRunner

D. LocalPipelineRunner

Correct Answer:B
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

Question 23

- (Exam Topic 6)
You’ve migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you’d like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you’d like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

A. Increase the size of your parquet files to ensure them to be 1 GB minimum.

B. Switch to TFRecords formats (app

C. 200MB per file) instead of parquet files.

D. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.

E. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.

Correct Answer:A

Question 24

- (Exam Topic 5)
Cloud Bigtable is a recommended option for storing very large amounts of _____?

A. multi-keyed data with very high latency

B. multi-keyed data with very low latency

C. single-keyed data with very low latency

D. single-keyed data with very high latency

Correct Answer:C
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview

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