Professional-Data-Engineer Exam Dumps - Google Cloud Certified Questions and Answers

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.

Reference Links:

Monitoring BigQuery Slots

BigQuery INFORMATION_SCHEMA

BigQuery Performance Best Practices

A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:

Materialized views, ML models in data warehouse - Google Cloud

Data Engineering with Google Cloud Platform - Packt Subscription

To ensure that messages failing to process in your Dataflow job are sent to a Pub/Sub topic for monitoring and alerting, the best approach is to use Pub/Sub’s dead-letter topic feature. Here’s why option C is the best choice:

Dead-Letter Topic:

Pub/Sub’s dead-letter topic feature allows messages that fail to be processed successfully to be redirected to a specified topic. This ensures that these messages are not lost and can be reviewed for debugging and alerting purposes.

Monitoring and Alerting:

By specifying a new Pub/Sub topic as the dead-letter topic, you can use Cloud Monitoring to track metrics such as subscription/dead_letter_message_count, providing visibility into the number of failed messages.

This allows you to set up alerts based on these metrics to notify the appropriate teams when failures occur.

Steps to Implement:

Enable Dead-Letter Topic:

Configure your Pub/Sub pull subscription to enable dead lettering and specify the new Pub/Sub topic for dead-letter messages.

Set Up Monitoring:

Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.

Configure alerts based on this metric to notify the team of any processing failures.

Reference Links:

Pub/Sub Dead Letter Policy

Cloud Monitoring with Pub/Sub