Free Google Professional-Data-Engineer Exam Questions

Question 1

You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

A : th batch and streaming

B : gQuery cannot be used as a sink

C : ly batch

D : ly streaming

Answer: A

Question 2

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

A : ansform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

B : ansform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

C : mpress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

D : mpress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

Answer: D

Question 3

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A : Cloud Deployment Manager to automate access provision.

B : troduce resource hierarchy to leverage access control policy inheritance.

C : eate distinct groups for various teams, and specify groups in Cloud IAM policies.

D : ly use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

E : r each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: A,C

Question 4

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud

Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a

Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to

run additional transformation jobs in BigQuery The transformation jobs are different for every table These

jobs might take hours to complete You need to determine the most efficient and maintainable workflow to

process hundreds of tables and provide the freshest data to your end users. What should you do?

A : 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage. Dataproc. and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline3 Schedule the DAG to run hourly

B : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators.2 Create a separate DAG for each table that needs to go through the pipeline3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

C : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Cloud Storage, Dataproc. and BigQuery operators2 Create a separate DAG for each table that needs to go through the pipeline3 Schedule the DAGs to run hourly

D : 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks byusing the Dataproc and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline.3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Answer: B

Question 5

You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues,

including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent

formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

A : Use Data Fusion to transform the data before loading it into BigQuery.

B : Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.

C : Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.

D : Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

Answer: A