Free Google Professional-Data-Engineer Exam Questions

Question 1

You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

A : Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

B : Adopt multi-regional Cloud Storage buckets in your architecture.

C : Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

D : Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.

Answer: D

Question 2

You are part of a healthcare organization where data is organized and managed by respective data owners in

various storage services. As a result of this decentralized ecosystem, discovering and managing data has

become difficult You need to quickly identify and implement a cost-optimized solution to assist your

organization with the following

• Data management and discovery

• Data lineage tracking

• Data quality validation

How should you build the solution?

A : Use BigLake to convert the current solution into a data lake architecture.

B : Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking

C : Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation

D : Use Dataplex to manage data, track data lineage, and perform data quality validation.

Answer: D

Question 3

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third

month the schema of the files changes. Your requirements for implementing these transformations include:

Executing the transformations on a schedule

Enabling non-developer analysts to modify transformations

Providing a graphical tool for designing transformations

What should you do?

A : Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

B : ad each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

C : lp the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

D : Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Answer: D

Question 4

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A : eate a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

B : eate a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

C : eate a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

D : eate an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: D

Question 5

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low. You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A : troduce data compression for each file to increase the rate file of file transfer.

B : ntact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

C : design the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

D : emble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

E : eate an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

Answer: A