Most Recent Google Professional-Data-Engineer Exam Questions & 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.

Monitoring BigQuery Slots

BigQuery INFORMATION_SCHEMA

BigQuery Performance Best Practices

This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately.Reference:

Exporting table data | BigQuery | Google Cloud

Storage classes | Cloud Storage | Google Cloud

Retention policies and retention periods | Cloud Storage | Google Cloud

Federated queries | BigQuery | Google Cloud

To design a data mesh architecture using Dataplex to eliminate bottlenecks caused by a central data platform team, consider the following:

Data Mesh Architecture:

Data mesh promotes a decentralized approach where domain teams manage their own data pipelines and assets, increasing agility and reducing bottlenecks.

Dataplex Lakes and Zones:

Lakes in Dataplex are logical containers for managing data at scale, and zones are subdivisions within lakes for organizing data based on domains, teams, or other criteria.

Domain and Team Management:

By creating a lake for each team and zones for each domain, each team can independently manage their data assets without relying on the central data platform team.

This setup aligns with the principles of data mesh, promoting ownership and reducing delays in data processing and insights.

Implementation Steps:

Create Lakes and Zones:

Create separate lakes in Dataplex for each team (analytics and data science).

Within each lake, create zones for the different domains (airlines, hotels, ride-hailing).

Attach BigQuery Datasets:

Attach the BigQuery datasets created by the respective teams as assets to their corresponding zones.

Decentralized Management:

Allow each domain to manage their own zone's data assets, providing them with the autonomy to update and maintain their pipelines without depending on the central team.

Dataplex Documentation

BigQuery Documentation

Data Mesh Principles