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Exam Professional-Data-Engineer Price | Dump Professional-Data-Engineer File

Exam Professional-Data-Engineer Price | Dump Professional-Data-Engineer File

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Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.

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Google Certified Professional Data Engineer Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the dat a. What should you do?

  • A. 1. Create a Cloud Storage bucket in the US multi-region.
    2. Run the Dataproc cluster in a zone in the ua-central1 region, reading data from the US multi-region bucket.
    3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.
  • B. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
    2. Enable turbo replication.
    3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.
    4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.
  • C. 1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region.
    2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.
    3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.
    4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.
  • D. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
    2. Enable turbo replication.
    3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region.
    4. In case of a regional failure, redeploy your Dataproc duster to the us-south1 region and continue reading from the same bucket.

Answer: B

Explanation:
To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice:
Dual-Region Bucket:
A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.
The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.
Turbo Replication:
Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.
This minimizes data loss in case of a regional failure.
Running Dataproc Cluster:
Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.
In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.
Steps to Implement:
Create a Dual-Region Bucket:
Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.
Enable turbo replication to ensure rapid data replication between the regions.
Deploy Dataproc Cluster:
Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.
Set Up Failover Plan:
Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us-central1 region.
Ensure that the failover process is well-documented and tested to minimize downtime and data loss.
Reference:
Google Cloud Storage Dual-Region
Turbo Replication in Google Cloud Storage
Dataproc Documentation


NEW QUESTION # 152
You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute) The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

  • A. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
  • B. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
  • C. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
  • D. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.

Answer: A


NEW QUESTION # 153
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. Update SQL pipelines and ad-hoc queries to run as interactive query jobs.
  • B. Update SOL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.
  • C. Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.
  • D. Increase the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.

Answer: B

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 # 154
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat

  • A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
  • B. Re-write the application to load accumulated data every 2 minutes.
  • C. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
  • D. Convert the streaming insert code to batch load for individual messages.
  • E. How can you adjust your application design?

Answer: E


NEW QUESTION # 155
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

  • A. Use the LIMIT keyword to reduce the number of rows returned.
  • B. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
  • C. Recreate the table with a partitioning column and clustering column.
  • D. Create a separate table for each ID.

Answer: C


NEW QUESTION # 156
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