Free Google Professional-Data-Engineer Exam Questions

Question 1

You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A : ollection

B : ansform

C : peline

D : nk API

Answer: B

Question 2

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 3

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service

provider. You need to create a data model for customers, products, and subscriptions All customers, products,

and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to

use the visualization layer for current and historical reporting. You need to ensure that the data model is

simple, easy-to-use. and cost-effective. What should you do?

A : Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

B : Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

C : Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data

D : Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

Answer: D

Question 4

An aerospace company uses a proprietary data format to store its night dat

a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A : a standard Dataflow pipeline to store the raw data in BigQuery and then transform the format later when the data is used.

B : ite a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

C : Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

D : an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

Answer: D

Question 5

A insurance claim review company provides expert opinion on contested insurance claims. The company uses Google Cloud for it's data analysis pipelines. Clients of the company upload documents to Cloud Storage. When a file is uploaded, the company wants to immediately move the files to a Classified Data bucket if the file contains personally identifying information. What method would you recommend to accomplish this?

A : Create a quarantine bucket for uploading, once a file is uploaded trigger a Cloud Function to call a custom built machine learning model trained to detect PII. If PII is detected, move the file to the Classified Data bucket.

B : Create a quarantine bucket for uploading, once a file is uploaded trigger a Cloud Function to call the Data Loss Prevention API to apply infotypes to detect PII. If PII is detected, move file to the Classified Data bucket.

C : Create a quarantine bucket for uploading, use Cloud Scheduler to run a job to run hourly that will call a custom built machine learning model trained to detect PII. If PII is detected, move file to the Classified Data bucket.

D : Create a quarantine bucket for uploading, use Cloud Scheduler to run a job to run hourly that will call the Data Loss Prevention API to apply infotypes to detect PII. If PII is detected, move file to the Classified Data bucket.

Answer: B