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PROFESSIONAL-DATA-ENGINEER Real Exam Questions

Professional Data Engineer on Google Cloud Platform

331 questions available · Page 1 of 34

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Question 1 Single choice

Which action can a Cloud Dataproc Viewer perform?

  1. A

    Submit a job.

  2. B

    Create a cluster.

  3. C

    Delete a cluster.

  4. D

    List the jobs.

Show answer and explanation

Correct answer: D

Explanation

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
References:
https://cloud.google.com/dataproc/docs/concepts/iam#iam_roles_and_cloud_dataproc_ope
rations_summary

Question 2 Single choice

You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data.

What should you do?

  1. A

    Increase the size of the training dataset, and increase the number of input features.

  2. B

    Increase the size of the training dataset, and decrease the number of input features.

  3. C

    Reduce the size of the training dataset, and increase the number of input features.

  4. D

    Reduce the size of the training dataset, and decrease the number of input features.

Show answer and explanation

Correct answer: B

Explanation

https://machinelearningmastery.com/impact-of-dataset-size-on-deep-learning-model-skill-and-performance-estimates/

Question 3 Single choice

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data.

What should you do?

  1. A

    Add a SideInput that returns a Boolean if the element is corrupt.

  2. B

    Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

  3. C

    Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

  4. D

    Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Show answer and explanation

Correct answer: B

Question 4 Single choice

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

The user profile: What the user likes and doesn't like to eat The user account information: Name, address, preferred meal times The order information: When orders are made, from where, to whom The database will be used to store all the transactional data of the product. You want to optimize the data schema.

Which Google Cloud Platform product should you use?

  1. A

    BigQuery

  2. B

    Cloud SQL

  3. C

    Cloud Bigtable

  4. D

    Cloud Datastore

Show answer and explanation

Correct answer: A

Question 5 Single choice

You have an Oracle database deployed in a VM as part of a Virtual Private Cloud (VPC) network. You want to replicate and continuously synchronize 50 tables to BigQuery. You want to minimize the need to manage infrastructure.

What should you do?

  1. A

    Create a Datastream service from Oracle to BigQuery, use a private connectivity configuration to the same VPC network, and a connection profile to BigQuery.

  2. B

    Create a Pub/Sub subscription to write to BigQuery directly Deploy the Debezium Oracle connector to capture changes in the Oracle database, and sink to the Pub/Sub topic.

  3. C

    Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle Change Data Capture (CDC), and Dataflow to stream the Kafka topic to BigQuery.
    D O Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle change data capture (CDC), and the Kafka Connect Google BigQuery Sink Connector.

Show answer and explanation

Correct answer: A

Explanation

Datastream is a serverless, scalable, and reliable service that enables you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage, and Cloud Pub/Sub. Datastream captures and streams database changes using change data capture (CDC) technology. Datastream supports private connectivity to the source and destination systems using VPC networks. Datastream also provides a connection profile to BigQuery, which simplifies the configuration and management of the data replication.
References:
Datastream overview Creating a Datastream stream Using Datastream with BigQuery

Question 6 Single choice

You need to modernize your existing on-premises data strategy. Your organization currently uses.

1. Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop
Distributed File System (HDFS) for data replication.
2. Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.

You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes.

What should you do?

  1. A

    Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.

  2. B

    Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.

  3. C

    Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

  4. D

    Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..

Show answer and explanation

Correct answer: D

Explanation

Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allows you to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms.
Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.
The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets. However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI.
References:
Dataproc overview Cloud Storage connector for Hadoop Cloud Composer overview

Question 7 Single choice

You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once, and must be ordered within windows of 1 hour.

How should you design the solution?

  1. A

    Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.

  2. B

    Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.

  3. C

    Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

  4. D

    Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.

Show answer and explanation

Correct answer: D

Question 8 Single choice

You are testing a Dataflow pipeline to ingest and transform text files.
The files are compressed gzip, errors are written to a dead-letter queue, and you are using Sidelnputs to join data You noticed that the pipeline is taking longer to complete than expected, what should you do to expedite the Dataflow job?

  1. A

    Switch to compressed Avro files

  2. B

    Reduce the batch size

  3. C

    Retry records that throw an error

  4. D

    Use CoGroupByKey instead of the Sidelnput

Show answer and explanation

Correct answer: B

Question 9 Single choice

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project.

How should you maintain users' privacy?

  1. A

    Grant the consultant the Viewer role on the project.

  2. B

    Grant the consultant the Cloud Dataflow Developer role on the project.

  3. C

    Create a service account and allow the consultant to log on with it.

  4. D

    Create an anonymized sample of the data for the consultant to work with in a different project.

Show answer and explanation

Correct answer: C

Question 10 Single choice

You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources.

What should you do?

  1. A

    Deploy the Cloud SQL Proxy on the Cloud Dataproc master

  2. B

    Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

  3. C

    Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

  4. D

    Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

Show answer and explanation

Correct answer: D