Free Google Professional-Data-Engineer Exam Questions

Question 1

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 2

Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

A : Google Stackdriver Audit Logs to review data access.

B : t the identity and access management IIAM) policy of each table

C : Stackdriver Monitoring to see the usage of BigQuery query slots.

D : the Google Cloud Billing API to see what account the warehouse is being billed to.

Answer: A

Question 3

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 : plement clustering in BigQuery on the ingest date column.

B : plement clustering in BigQuery on the package-tracking ID column.

C : er older data onto Cloud Storage files, and leverage extended tables.

D : -create the table using data partitioning on the package delivery date.

Answer: A

Question 4

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 5

You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag

template named "gdpr". The template contains one mandatory field, "has sensitive data~. with a boolean value.

All employees must be able to do a simple search and find tables in the dataset that have either true or false in

the "has sensitive data" field. However, only the Human Resources (HR) group should be able to see the data

inside the tables for which "hass-ensitive-data" is true. You give the all employees group the

bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize

configuration overhead. What should you do next?

A : Create the "gdpr" tag template with private visibility. Assign the bigquery -dataViewer role to the HRgroup on the tables that contain sensitive data.

B : Create the ~gdpr" tag template with private visibility. Assign the datacatalog. tagTemplateViewer roleon this tag to the all employeesgroup, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

C : Create the "gdpr" tag template with public visibility. Assign the bigquery. dataViewer role to the HRgroup on the tables that containsensitive data.

D : Create the "gdpr" tag template with public visibility. Assign the datacatalog. tagTemplateViewer role onthis tag to the all employees.group, and assign the bijquery.dataViewer role to the HR group on the tables that contain sensitive data.

Answer: D