100% PASS QUIZ USEFUL GOOGLE - PROFESSIONAL-DATA-ENGINEER - LATEST GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM EXAM QUESTIONS VCE

100% Pass Quiz Useful Google - Professional-Data-Engineer - Latest Google Certified Professional Data Engineer Exam Exam Questions Vce

100% Pass Quiz Useful Google - Professional-Data-Engineer - Latest Google Certified Professional Data Engineer Exam Exam Questions Vce

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Before you decide to get the Professional-Data-Engineer exam certification, you may be attracted by the benefits of Professional-Data-Engineer credentials. Get certified by Professional-Data-Engineer certification means you have strong professional ability to deal with troubleshooting in the application. Besides, you will get promotion in your job career and obtain a higher salary. If you want to pass your Google Professional-Data-Engineer Actual Test at first attempt, Professional-Data-Engineer pdf torrent is your best choice. The high pass rate of Professional-Data-Engineer vce dumps can give you surprise.

Google Professional-Data-Engineer certification exam is designed to test the knowledge and skills of candidates in the field of data engineering. Professional-Data-Engineer exam is intended for professionals who are responsible for designing, building, and maintaining data processing systems. Professional-Data-Engineer exam is designed to validate the candidate's ability to use Google Cloud Platform technologies to design and implement data processing systems, to build and maintain data structures and databases, and to analyze and optimize data processing workflows.

The Google Professional-Data-Engineer Exam Tests the candidate's proficiency in working with various data processing tools, including Google Cloud Platform technologies such as BigQuery, Dataflow, and Cloud Storage. Professional-Data-Engineer exam also covers topics such as data modeling, data ingestion, data transformation, and data analysis. Additionally, the exam tests the candidate's understanding of best practices for data engineering, including performance optimization, security, and compliance.

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Google Certified Professional Data Engineer Exam Sample Questions (Q321-Q326):

NEW QUESTION # 321
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

  • A. Windows
  • B. OutputCriteria
  • C. Triggers
  • D. Sessions

Answer: C

Explanation:
Explanation
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Trig


NEW QUESTION # 322
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

  • A. Windows
  • B. OutputCriteria
  • C. Triggers
  • D. Sessions

Answer: C

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.


NEW QUESTION # 323
What are two of the characteristics of using online prediction rather than batch prediction?

  • A. It is optimized to handle a high volume of data instances in a job and to run more complex models.
  • B. Predictions are written to output files in a Cloud Storage location that you specify.
  • C. Predictions are returned in the response message.
  • D. It is optimized to minimize the latency of serving predictions.

Answer: C,D

Explanation:
Online prediction
Optimized to minimize the latency of serving predictions.
Predictions returned in the response message.
Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models.
Predictions written to output files in a Cloud Storage location that you specify.
Reference: https://cloud.google.com/ml-engine/docs/prediction-
overview#online_prediction_versus_batch_prediction


NEW QUESTION # 324
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

  • A. Google Cloud Datastore
  • B. Cloud Bigtable
  • C. Google Cloud Storage
  • D. Google BigQuery

Answer: B

Explanation:
Explanation/Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series


NEW QUESTION # 325
You are administering shared BigQuery datasets that contain views used by multiple teams in your organization. The marketing team is concerned about the variability of their monthly BigQuery analytics spend using the on-demand billing model. You need to help the marketing team establish a consistent BigQuery analytics spend each month. What should you do?

  • A. Create a BigQuery Standard pay-as-you go reservation with a baseline of 0 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.
  • B. Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.
  • C. Establish a BigQuery quota for the marketing team, and limit the maximum number of bytes scanned each day.
  • D. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.

Answer: D

Explanation:
To help the marketing team establish a consistent BigQuery analytics spend each month, you can use BigQuery reservations to allocate dedicated slots for their queries. This provides predictable costs by reserving a fixed amount of compute resources.
BigQuery Reservations:
BigQuery Reservations allow you to purchase dedicated query processing capacity in the form of slots.
By reserving slots, you can control costs and ensure that the marketing team has the necessary resources for their queries without unexpected increases in spending.
Baseline Slots:
Setting a baseline of 500 slots without autoscaling ensures a consistent allocation of resources.
This provides a predictable monthly cost, as the marketing team will be billed for the reserved slots regardless of actual usage.
Billing Back:
The marketing team's usage can be billed back based on the fixed reservation cost, ensuring budget predictability.
This approach avoids the variability associated with on-demand billing, where costs can fluctuate based on query volume and complexity.
No Autoscaling:
By not enabling autoscaling, you prevent additional costs from being incurred due to temporary increases in query demand.
This fixed reservation ensures that the marketing team only uses the allocated 500 slots, maintaining a consistent monthly spend.
Google Data Engineer Reference:
BigQuery Reservations Documentation
BigQuery Slot Reservations
Managing BigQuery Costs
Using a fixed reservation of 500 slots provides the marketing team with predictable costs and the necessary resources for their queries without unexpected billing variability.


NEW QUESTION # 326
......

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