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Google Updated Professional-Data-Engineer Exam Questions and Answers by stevie

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Google Professional-Data-Engineer Exam Overview :

Exam Name: Google Professional Data Engineer Exam
Exam Code: Professional-Data-Engineer Dumps
Vendor: Google Certification: Google Cloud Certified
Questions: 400 Q&A's Shared By: stevie
Question 32

You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?

Options:

A.

Add all application networks to Authorized Network and regularly update them.

B.

Add CIDR 0.0.0.0/0 network to Authorized Network. Use Identity and Access Management (1AM) to add users.

C.

Leave the Authorized Network empty. Use Cloud SQL Auth proxy on all applications.

D.

Add CIDR 0.0.0.0/0 network to Authorized Network. Use Cloud SOL Auth proxy on all applications.

Discussion
Question 33

Your company needs to ingest and transform streaming data from IoT devices and store it for analysis. The data is sensitive and requires encryption with your own key in transit and at rest. The volume of data is expected to fluctuate significantly throughout the day. You need to identify a solution that is managed and elastic. What should you do?

Options:

A.

Write data directly into BigQuery by using the Storage Write API, and process it in BigQuery by using SQL functions, selecting a Google-managed encryption key for each service.

B.

Publish data to Pub/Sub, process it with Dataflow and store it in Cloud SQL, selecting your key from Cloud HSM for each service.

C.

Publish data to Pub/Sub, process it with Dataflow and store it in BigQuery, selecting your key from Cloud KMS for each service.

D.

Write data directly into Cloud Storage, process it with Dataproc, and store it in BigQuery, selecting a customer-managed encryption key (CMEK) for each service.

Discussion
Question 34

You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:

• Data engineers, which require lull data lake access

• Analytic users, which require access to curated data

You need to assign access rights to these two groups. What should you do?

Options:

A.

1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake.2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.

B.

1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake.2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone.

C.

1. Grant the bigquery.dataownex role on BigQuery datasets and the storage.objectcreator role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.

D.

1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectviewer role on Cloud Storage buckets to data engineers.2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.

Discussion
Question 35

You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

Options:

A.

Use Cloud ML Engine for training existing Spark ML models

B.

Rewrite your models on TensorFlow, and start using Cloud ML Engine

C.

Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

D.

Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Discussion
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