Curious about Actual Google Cloud Certified Professional Data Engineer Exam Questions?

Correct : D

To ensure resilience and minimize the recovery point objective (RPO) with no impact on applications, using a dual-region bucket with turbo replication is the best approach. Here's why option D is the best choice:

Dual-Region Buckets:

Dual-region buckets store data redundantly across two distinct geographic regions, providing high availability and durability.

This setup ensures that data remains available even if one region experiences a failure.

Turbo Replication:

Turbo replication ensures that data is replicated between the two regions within 15 minutes, aligning with the requirement to minimize the recovery point objective (RPO).

This feature provides near real-time replication, significantly reducing the risk of data loss.

No Impact on Applications:

Applications continue to access the dual-region bucket without any changes, ensuring seamless operation even during a regional failure.

The dual-region setup transparently handles failover, providing uninterrupted access to data.

Steps to Implement:

Create a Dual-Region Bucket:

Create a dual-region Cloud Storage bucket in the Google Cloud Console, selecting appropriate regions (e.g., us-central1 and us-east1).

Enable Turbo Replication:

Enable turbo replication to ensure rapid data replication between the selected regions.

Configure Applications:

Ensure that applications read and write to the dual-region bucket, benefiting from its high availability and durability.

Test Failover:

Simulate a regional failure to verify that the dual-region bucket and turbo replication meet the required RPO and ensure data resilience.

Google Cloud Storage Dual-Region

Turbo Replication in Google Cloud Storage

Correct : A

Correct : A

To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:

Cloud Functions:

Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.

This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.

Flexibility and Simplicity:

Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.

Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.

Performance:

Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.

They are designed to scale automatically based on demand, ensuring that your workflow remains performant.

Steps to Implement:

Write the Cloud Function:

Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.

Deploy the function to Google Cloud.

Invoke Cloud Function from Workflow:

Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.

steps:

- callCloudFunction:

call: http.post

args:

url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME

body:

key: value

Process Results:

Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.

Google Cloud Functions Documentation

Using Workflows with Cloud Functions

Workflows Standard Library

Correct : B

To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here's why option B is the best choice:

Separate Reservations for SLA and Ad-hoc Projects:

Creating two separate reservations allows for dedicated resource management tailored to the needs of each project.

The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned.

Enterprise Edition Reservation for SLA Project:

Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources.

Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs.

On-Demand Billing for Ad-hoc Project:

Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity.

This model suits the less predictable and often lower-utilization nature of ad-hoc queries.

Steps to Implement:

Set Up Enterprise Edition Reservation for SLA Project:

Create a reservation with a baseline of 300 slots.

Enable autoscaling to allow up to an additional 500 slots as needed.

Configure On-Demand Billing for Ad-hoc Project:

Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries.

Monitor and Adjust:

Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary.

BigQuery Slot Reservations

BigQuery On-Demand Pricing

Correct : D

To troubleshoot query performance issues related to job queuing or slot contention in BigQuery, using administrative resource charts along with querying the INFORMATION_SCHEMA is the best approach. Here's why option D is the best choice:

Administrative Resource Charts:

BigQuery provides detailed resource charts that show slot usage and job performance over time. These charts help identify patterns of slot contention and peak usage times.

INFORMATION_SCHEMA Queries:

The INFORMATION_SCHEMA tables in BigQuery provide detailed metadata about query jobs, including execution times, slots consumed, and other performance metrics.

Running queries on INFORMATION_SCHEMA allows you to pinpoint specific jobs causing contention and analyze their performance characteristics.

Comprehensive Analysis:

Combining administrative resource charts with detailed queries on INFORMATION_SCHEMA provides a holistic view of the system's performance.

This approach enables you to identify and address the root causes of performance issues, whether they are due to slot contention, inefficient queries, or other factors.

Steps to Implement:

Access Administrative Resource Charts:

Use the Google Cloud Console to view BigQuery's administrative resource charts. These charts provide insights into slot utilization and job performance metrics over time.

Run INFORMATION_SCHEMA Queries:

Execute queries on BigQuery's INFORMATION_SCHEMA to gather detailed information about job performance. For example:

SELECT

creation_time,

job_id,

user_email,

query,

total_slot_ms / 1000 AS slot_seconds,

total_bytes_processed / (1024 * 1024 * 1024) AS processed_gb,

total_bytes_billed / (1024 * 1024 * 1024) AS billed_gb

FROM

`region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT

WHERE

creation_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY)

AND state = 'DONE'

ORDER BY

slot_seconds DESC

LIMIT 100;

Analyze and Optimize:

Use the information gathered to identify bottlenecks, optimize queries, and adjust resource allocations as needed to improve performance.

Monitoring BigQuery Slots

BigQuery INFORMATION_SCHEMA

BigQuery Performance Best Practices