RELIABLE EXAM PROFESSIONAL-DATA-ENGINEER PASS4SURE - EXAM PROFESSIONAL-DATA-ENGINEER REFERENCE

Reliable Exam Professional-Data-Engineer Pass4sure - Exam Professional-Data-Engineer Reference

Reliable Exam Professional-Data-Engineer Pass4sure - Exam Professional-Data-Engineer Reference

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Google Professional-Data-Engineer Certification Exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

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Google Certified Professional Data Engineer Exam Sample Questions (Q122-Q127):

NEW QUESTION # 122
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use
Hadoop jobs they have already created and minimize the management of the cluster as much as possible.
They also want to be able to persist data beyond the life of the cluster. What should you do?

  • A. Create a Google Cloud Dataflow job to process the data.
  • B. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
  • C. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
  • D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
  • E. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

Answer: A


NEW QUESTION # 123
The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?

  • A. Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.
  • B. Increase the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.
  • C. Update SOL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.
  • D. Update SQL pipelines and ad-hoc queries to run as interactive query jobs.

Answer: C

Explanation:
To resolve the concurrency issue in BigQuery caused by the introduction of hundreds of non-time-sensitive SQL pipelines, the best approach is to differentiate the types of queries based on their urgency and resource requirements. Here's why option C is the best choice:
SQL Pipelines as Batch Queries:
Batch queries in BigQuery are designed for non-time-sensitive operations. They run in a lower priority queue and do not consume slots immediately, which helps to reduce the overall slot consumption during peak times.
By converting non-time-sensitive SQL pipelines to batch queries, you can significantly alleviate the pressure on slot reservations.
Ad-Hoc Queries as Interactive Queries:
Interactive queries are prioritized to run immediately and are suitable for ad-hoc analysis where users expect quick results.
Running ad-hoc queries as interactive jobs ensures that analysts can get their results without delay, improving productivity and user satisfaction.
Concurrency Management:
This approach helps balance the workload by leveraging BigQuery's ability to handle different types of queries efficiently, reducing the likelihood of encountering quota errors due to slot exhaustion.
Steps to Implement:
Identify Non-Time-Sensitive Pipelines:
Review and identify SQL pipelines that are not time-critical and can be executed as batch jobs.
Update Pipelines to Batch Queries:
Modify these pipelines to run as batch queries. This can be done by setting the priority of the query job to BATCH.
Ensure Ad-Hoc Queries are Interactive:
Ensure that all ad-hoc queries are submitted as interactive jobs, allowing them to run with higher priority and immediate slot allocation.
Reference:
BigQuery Batch Queries
BigQuery Slot Allocation and Management


NEW QUESTION # 124
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

  • A. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
  • B. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
  • C. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
  • D. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Answer: A


NEW QUESTION # 125
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat
a. What should you do?

  • A. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
    Deploy the models using Cloud Dataproc. Call the model from your application.
  • B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
    classification model with Spark MLlib to filter results to match customer preferences. Deploy the models
    using Cloud Dataproc. Call the models from your application.
  • C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
  • D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Answer: C


NEW QUESTION # 126
Which is not a valid reason for poor Cloud Bigtable performance?

  • A. There are issues with the network connection.
  • B. The table's schema is not designed correctly.
  • C. The workload isn't appropriate for Cloud Bigtable.
  • D. The Cloud Bigtable cluster has too many nodes.

Answer: D

Explanation:
Explanation
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance


NEW QUESTION # 127
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