2025 100% FREE PROFESSIONAL-DATA-ENGINEER–100% FREE EXAM DEMO | GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM NEW QUESTIONS

2025 100% Free Professional-Data-Engineer–100% Free Exam Demo | Google Certified Professional Data Engineer Exam New Questions

2025 100% Free Professional-Data-Engineer–100% Free Exam Demo | Google Certified Professional Data Engineer Exam New Questions

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Google Certified Professional Data Engineer Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
You work for a manufacturing plant that batches application log files together into a single log file once a day at
2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

  • A. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
  • B. Manually start the Cloud Dataflow job each morning when you get into the office.
  • C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
  • D. Change the processing job to use Google Cloud Dataproc instead.

Answer: C


NEW QUESTION # 179
You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

  • A. Modify ETL job to load the data into both the current and another backup region.
  • B. Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
  • C. Schedule a daily BigQuery snapshot of the table.
  • D. Schedule a daily copy of the dataset to a backup region.

Answer: C

Explanation:
To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:
* Cloud Functions:
* Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.
* This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.
* Flexibility and Simplicity:
* Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.
* Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.
* Performance:
* Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.
* They are designed to scale automatically based on demand, ensuring that your workflow remains performant.
Steps to Implement:
* Write the Cloud Function:
* Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.
* Deploy the function to Google Cloud.
* Invoke Cloud Function from Workflow:
* Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.
steps:
- callCloudFunction:
call: http.post
args:
url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
body:
key: value
* Process Results:
* Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.
Reference Links:
* Google Cloud Functions Documentation
* Using Workflows with Cloud Functions
* Workflows Standard Library


NEW QUESTION # 180
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

  • A. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
  • B. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic
  • C. Use gsutil cp -Jto compress the content being uploaded to Cloud Storage
  • D. Use Transfer Appliance to copy the data to Cloud Storage

Answer: D

Explanation:
Explanation


NEW QUESTION # 181
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/ Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket.
The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

  • A. An alert based on a decrease of instance/storage/used_bytesfor the source and a rate of change increase of subscription/num_undelivered_messages for the destination
  • B. An alert based on a decrease of subscription/num_undelivered_messagesfor the source and a rate of change increase of instance/storage/used_bytesfor the destination
  • C. An alert based on an increase of subscription/num_undelivered_messagesfor the source and a rate of change decrease of instance/storage/used_bytesfor the destination
  • D. An alert based on an increase of instance/storage/used_bytesfor the source and a rate of change decrease of subscription/num_undelivered_messages for the destination

Answer: C


NEW QUESTION # 182
Which software libraries are supported by Cloud Machine Learning Engine?

  • A. Theano and Torch
  • B. TensorFlow
  • C. Theano and TensorFlow
  • D. TensorFlow and Torch

Answer: B

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.


NEW QUESTION # 183
......

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