Google Professional Data Engineer Exam Questions Instant Access

Question 1

You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.

Which service do you select for storing and serving your data?

ACloud Spanner

BCloud Bigtable

CCloud Firestore

DCloud SQL

Answer : D

Question 2

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

ABuild and train a complex classification model with Spark MLlib to generate labels and filter the results.Deploy the models using Cloud Dataproc. Call the model from your application.

BBuild and train a classification model with Spark MLlib to generate labels. Build and train a secondclassification model with Spark MLlib to filter results to match customer preferences. Deploy the modelsusing Cloud Dataproc. Call the models from your application.

CBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in CloudBigtable, and filter the predicted labels to match the user's viewing history to generate preferences.

DBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in CloudSQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Answer : C

Question 3

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.

They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

ABigQuery

BCloud SQL

CCloud BigTable

DCloud Datastore

Answer : 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?

A1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using 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

B1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using 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

C1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using 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

D1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using 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

This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket.By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performanceof these services12.By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3.By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for thecorresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4.

Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket.Reference:

1: Dataproc Operator | Cloud Composer | Google Cloud

2: BigQuery Operator | Cloud Composer | Google Cloud

3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud

4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud

[5]: Triggering DAGs | Cloud Composer | Google Cloud

[6]: Cloud Storage Operator | Cloud Composer | Google Cloud

Question 5

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

AModify the transformMapReduce jobs to apply sensor calibration before they do anything else.

BIntroduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

CAdd sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

DDevelop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Answer : A

Question 6

You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

AUse Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.

BUse Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.

CUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.

DUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

Answer : D

Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project.Reference:https://cloud.google.com/composer/docs/how-to/managing/configuring-shared-vpc

Question 7

Google Cloud Bigtable indexes a single value in each row. This value is called the _______.

Aprimary key

Bunique key

Crow key

Dmaster key

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.

Google Cloud Certified Professional Data Engineer Exam Questions