Google exactexam professional-data-engineer Questions Bank by Elowen q161 vce pdf

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Exam Name:	Google Professional Data Engineer Exam
Exam Code:	Professional-Data-Engineer Dumps
Vendor:	Google	Certification:	Google Cloud Certified
Questions:	376 Q&A's	Shared By:	elowen

Question 28

You are using Workflows to call an API that returns a 1 KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?

Options:

Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.

Invoke a subworkflow in Workflows to apply the logic on your JSON file.

Create a Cloud Composer environment and run the logic in Cloud Composer.

Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.

Discussion

Question 29

You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

Options:

1. Store the historical data in BigQuery for analytics.

2. In a Cloud SQL table, store the last state of the product after every product change.

3. Serve the last state data directly from Cloud SQL to the API.

1. Store the historical data in Cloud SQL for analytics.

2. In a separate table, store the last state of the product after every product change.

3. Serve the last state data directly from Cloud SQL to the API.

1. Store the products as a collection in Firestore with each product having a set of historical changes.

2. Use simple and compound queries for analytics.

3. Serve the last state data directly from Firestore to the API.

1. Store the historical data in BigQuery for analytics.

2. Use a materialized view to precompute the last state of a product.

3. Serve the last state data directly from BigQuery to the API.

Discussion

Question 30

You have a variety of files in Cloud Storage that your data science team wants to use in their models Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

Options:

Load the data into BigQuery and use SQL to transform the data as necessary Provide the data science team access to staging tables to explore the raw data.

Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.

Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.

Create an external table in BigQuery and use SQL to transform the data as necessary Provide the data science team access to the external tables to explore the raw data.

Discussion

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Question 31

You are creating the CI'CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

Options:

1. Use Cloud Build to build a container and the Kubemetes Pod Operator to deploy the code of the DAG to the Google Kubernetes

Engine (GKE) cluster of the development instance for testing.

2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.

1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.

2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

1 Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing.

2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.

1 Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing.

2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.

Discussion

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