Google Cloud Certified Professional-Data-Engineer Release Date

Google Professional Data Engineer Exam Questions and Answers

Question 57

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

• Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.

• 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?

Options:

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

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

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.

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

Answer:

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 allowsyou 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 58

You are building a teal-lime prediction engine that streams files, which may contain Pll (personal identifiable information) data, into Cloud Storage and eventually into BigQuery You want to ensure that the sensitive data is masked but still maintains referential Integrity, because names and emails are often used as join keys How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the Pll data is not accessible by unauthorized individuals?

Options:

Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non-tokenized data in a locked-down button.

Redact all Pll data, and store a version of the unredacted data in a locked-down bucket

Scan every table in BigQuery, and mask the data it finds that has Pll

Create a pseudonym by replacing Pll data with a cryptographic format-preserving token

Question 59

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?

Options:

Deploy the Cloud SQL Proxy on the Cloud Dataproc master

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

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

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

Question 60

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have

asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Options:

Increase the CPU size on your server.

Increase the size of the Google Persistent Disk on your server.

Increase your network bandwidth from your datacenter to GCP.

Increase your network bandwidth from Compute Engine to Cloud Storage.

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Google Cloud Certified Professional-Data-Engineer Release Date

Google Professional Data Engineer Exam Questions and Answers

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