Google spoto sure Pass Exam Professional-data-engineer Pdf by Josh q241 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:	330 Q&A's	Shared By:	josh

Question 12

Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

Options:

Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.

Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.

Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.

Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

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Question 13

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Options:

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

Discussion

Question 14

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

Question 15

You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

Options:

Use your model to run predictions on fresh customer input data.

Test and evaluate your model on your curated data to determine how well the model performs.

Monitor your model performance, and make any adjustments needed.

Delineate what data will be used for testing and what will be used for training the model.