GOOGLE PROFESSIONAL-DATA-ENGINEER: GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM TEST QUESTIONS - LEAD2PASS PASS EXAM

Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam test questions - Lead2pass pass exam

Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam test questions - Lead2pass pass exam

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Google Certified Professional Data Engineer Exam Sample Questions (Q244-Q249):

NEW QUESTION # 244
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

  • A. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
  • B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
  • C. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
  • D. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Answer: D


NEW QUESTION # 245
You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

  • A. Exceptions in worker code
  • B. Graph or pipeline construction
  • C. Job validation
  • D. Insufficient permissions

Answer: A

Explanation:
https://cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline#detect_an_exception_in_worker_code While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface.


NEW QUESTION # 246
You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

  • A. Enable the Object Versioning feature. Create a copy in a bucket in a different region.
  • B. Enable the Object Versioning feature. Add a lifecycle rule.
  • C. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
  • D. Set a retention policy. Lock the retention policy.

Answer: D

Explanation:
To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:
Retention Policy:
A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified. This ensures data immutability.
Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.
Locking the Retention Policy:
Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.
Steps to Implement:
Set the Retention Policy:
Define a retention period for the bucket to ensure that all objects are protected for the required duration.
Lock the Retention Policy:
Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.
Reference:
Cloud Storage Retention Policy Documentation
How to Set a Retention Policy


NEW QUESTION # 247
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

  • A. Write a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
  • B. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format
  • C. Use a standard Dataflow pipeline to store the raw data in BigQuery and then transform the format later when the data is used.
  • D. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

Answer: B


NEW QUESTION # 248
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

  • A. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
  • B. Increase the cluster size with more non-preemptible workers.
  • C. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
  • D. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Answer: C

Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex


NEW QUESTION # 249
......

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