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Answer : C

Answer : A

To minimize the cost of storing and retrieving objects in a Cloud Storage bucket while ensuring that cost optimization efforts are transparent to the users and applications, enabling Autoclass is the best approach. Here's why:

Autoclass Feature:

Autoclass automatically transitions objects between different storage classes (Standard, Nearline, Coldline, and Archive) based on their access patterns.

It ensures that frequently accessed data is kept in lower-latency, higher-cost storage classes and infrequently accessed data is moved to higher-latency, lower-cost storage classes.

Cost Optimization:

Autoclass optimizes storage costs by automatically moving objects to the most cost-effective storage class based on actual usage patterns, without manual intervention.

This feature ensures that objects are stored in the most economical class appropriate for their access frequency, reducing storage costs over time.

Transparency to Users:

The transition of objects between storage classes is handled automatically by Cloud Storage, making the process transparent to users and applications.

Users and applications interact with the objects in the same way, regardless of the underlying storage class, ensuring seamless access.

Steps to Implement:

Create a Cloud Storage Bucket:

When creating a new Cloud Storage bucket, enable the Autoclass feature.

Configure Autoclass:

Autoclass configuration is typically a straightforward process in the Google Cloud Console, where you enable it during bucket creation.

Monitor and Adjust:

Monitor the storage and access patterns through the Google Cloud Console to ensure that Autoclass is optimizing costs as expected.

Google Cloud Storage Autoclass

Optimizing Storage Costs with Autoclass

Answer : D

To simulate a Redis instance failover in a production-like environment without impacting production data, the best approach is to use a development environment. Here's why option D is the best choice:

Standard Tier Memorystore for Redis:

The Standard Tier provides high availability and automatic failover capabilities. It's suitable for testing failover scenarios in a controlled environment.

Development Environment:

Using a development environment ensures that any potential data loss or impact from the failover simulation does not affect production data, maintaining the integrity and availability of the production system.

Limited-Data-Loss Mode:

The limited-data-loss mode for manual failover ensures that data loss is minimized during the failover process, making it a realistic simulation of a production failover scenario.

Steps to Implement:

Create a Development Environment:

Set up a development environment with a Standard Tier Memorystore for Redis instance that mirrors the configuration of your production instance.

Initiate Manual Failover:

Initiate a manual failover using the limited-data-loss data protection mode to simulate a failover scenario:

gcloud redis instances failover INSTANCE_ID --data-protection-mode=limited-data-loss

Verify Failover:

Monitor and verify the failover process to ensure it behaves as expected, simulating the disaster recovery scenario accurately.

Memorystore for Redis Documentation

Manual Failover in Memorystore

Answer : A

To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:

Materialized Views:

Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.

They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.

Efficiency and Cost Reduction:

By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.

Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.

Minimized Maintenance:

Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.

Steps to Implement:

Identify Aggregation Queries:

Analyze the existing queries to identify common aggregation patterns at the day and month levels.

Create Materialized Views:

Create materialized views in BigQuery for the identified aggregation patterns. For example

CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS

SELECT

DATE(transaction_time) AS day,

SUM(amount) AS total_sales

FROM

project.dataset.sales

GROUP BY

day;

CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS

SELECT

EXTRACT(YEAR FROM transaction_time) AS year,

EXTRACT(MONTH FROM transaction_time) AS month,

SUM(amount) AS total_sales

FROM

project.dataset.sales

GROUP BY

year, month;

Query Using Materialized Views:

Update existing queries to use the materialized views instead of directly querying the base table.

BigQuery Materialized Views

Optimizing Query Performance

Answer : A

To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here's why option A is the best choice:

Cloud Data Loss Prevention (DLP):

Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre-defined infoTypes for various kinds of sensitive data, including street addresses.

Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.

Deep Inspection Job:

A deep inspection job allows you to scan entire tables for sensitive information.

By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.

Scalability and Accuracy:

Cloud DLP is scalable and can handle large datasets efficiently.

It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.

Steps to Implement:

Set Up Cloud DLP:

Enable the Cloud DLP API in your Google Cloud project.

Create an Inspection Template:

Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.

Run Deep Inspection Jobs:

Create and run a deep inspection job for each table in your dataset using the inspection template.

Review the inspection job results to retrieve all occurrences of street addresses.

Cloud DLP Documentation

Creating Inspection Jobs