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Unlock Your Potential with Google Professional-Data-Engineer Exam Questions

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Google Certified Professional Data Engineer Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

A. The maximum number of workers
B. The zone
C. The disk size per worker
D. The number of workers

Answer: B

NEW QUESTION # 182
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non- key columns. What should you do?

A. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
B. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
D. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

Answer: B

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 183
A web server sends click events to a Pub/Sub topic as messages. The web server includes an event Timestamp attribute in the messages, which is the time when the click occurred. You have a Dataflow streaming job that reads from this Pub/Sub topic through a subscription, applies some transformations, and writes the result to another Pub/Sub topic for use by the advertising department. The advertising department needs to receive each message within 30 seconds of the corresponding click occurrence, but they report receiving the messages late. Your Dataflow job's system lag is about 5 seconds, and the data freshness is about 40 seconds. Inspecting a few messages show no more than 1 second lag between their event Timestamp and publish Time. What is the problem and what should you do?

A. Messages in your Dataflow job are taking more than 30 seconds to process. Optimize your job or increase the number of workers to fix this.
B. Messages in your Dataflow job are processed in less than 30 seconds, but your job cannot keep up with the backlog in the Pub/Sub subscription. Optimize your job or increase the number of workers to fix this.
C. The advertising department is causing delays when consuming the messages. Work with the advertising department to fix this.
D. The web server is not pushing messages fast enough to Pub/Sub. Work with the web server team to fix this.

Answer: B

Explanation:
To ensure that the advertising department receives messages within 30 seconds of the click occurrence, and given the current system lag and data freshness metrics, the issue likely lies in the processing capacity of the Dataflow job. Here's why option B is the best choice:
System Lag and Data Freshness:
The system lag of 5 seconds indicates that Dataflow itself is processing messages relatively quickly.
However, the data freshness of 40 seconds suggests a significant delay before processing begins, indicating a backlog.
Backlog in Pub/Sub Subscription:
A backlog occurs when the rate of incoming messages exceeds the rate at which the Dataflow job can process them, causing delays.
Optimizing the Dataflow Job:
To handle the incoming message rate, the Dataflow job needs to be optimized or scaled up by increasing the number of workers, ensuring it can keep up with the message inflow.
Steps to Implement:
Analyze the Dataflow Job:
Inspect the Dataflow job metrics to identify bottlenecks and inefficiencies.
Optimize Processing Logic:
Optimize the transformations and operations within the Dataflow pipeline to improve processing efficiency.
Increase Number of Workers:
Scale the Dataflow job by increasing the number of workers to handle the higher load, reducing the backlog.
Reference:
Dataflow Monitoring
Scaling Dataflow Jobs

NEW QUESTION # 184
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log dat a. How should you set up the log data transfer into Google Cloud?

A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
B. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
D. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Answer: C

Explanation:
storage bucket as a final destination.

NEW QUESTION # 185
You are building a model to make clothing recommendations. You know a user's fashion preference is
likely to change over time, so you build a data pipeline to stream new data back to the model as it
becomes available. How should you use this data to train the model?

A. Continuously retrain the model on just the new data.
B. Train on the existing data while using the new data as your test set.
C. Train on the new data while using the existing data as your test set.
D. Continuously retrain the model on a combination of existing data and the new data.

Answer: C

NEW QUESTION # 186
......

Professional-Data-Engineer Valid Test Syllabus: https://www.validexam.com/Professional-Data-Engineer-latest-dumps.html

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