GOOGLE PROFESSIONAL-DATA-ENGINEER QUESTIONS - LATEST PROFESSIONAL-DATA-ENGINEER DUMPS [2025]

Google Professional-Data-Engineer Questions - Latest Professional-Data-Engineer Dumps [2025]

Google Professional-Data-Engineer Questions - Latest Professional-Data-Engineer Dumps [2025]

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Google Certified Professional Data Engineer Exam Sample Questions (Q348-Q353):

NEW QUESTION # 348
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  • A. Deploy small Kafka clusters in your data centers to buffer events.
  • B. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • C. Establish a Cloud Interconnect between all remote data centers and Google.
  • D. Have the data acquisition devices publish data to Cloud Pub/Sub.

Answer: A


NEW QUESTION # 349
What are two methods that can be used to denormalize tables in BigQuery?

  • A. 1) Use nested repeated fields; 2) Use a partitioned table
  • B. 1) Use a partitioned table; 2) Join tables into one table
  • C. 1) Split table into multiple tables; 2) Use a partitioned table
  • D. 1) Join tables into one table; 2) Use nested repeated fields

Answer: D

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-
warehouse#denormalizing_data


NEW QUESTION # 350
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL `dataset.model', table . How should you create the ML pipeline?
user_features)

  • A. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
  • B. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
  • C. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
    Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
  • D. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.

Answer: C


NEW QUESTION # 351
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

  • A. Change the pipeline code, and introduce a Reshuffle step to prevent fusion.
  • B. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
  • C. Update the job to increase the maximum number of workers.
  • D. Enable Vertical Autoscaling to let the pipeline use larger workers.

Answer: B

Explanation:
Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage. This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. Reference:
1: Streaming pipelines
2: Batch vs Streaming Performance in Google Cloud Dataflow
3: Deploy Dataflow pipelines
4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
5: Managing costs for Dataflow batch and streaming data processing


NEW QUESTION # 352
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?

  • A. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
  • B. Put the data into Google Cloud Storage.
  • C. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
  • D. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Answer: D

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/dataproc/


NEW QUESTION # 353
......

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