Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

https://towardsdatascience.com/understanding-hyperparameters-and-its-optimisation-techniques-f0debba07568

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.

Reference Links:

BigQuery Materialized Views

Optimizing Query Performance

Comprehensive and Detailed Explanation:

The core requirement is to make specific data values permanently unreadable, a concept often referred to as cryptographic erasure or "crypto-shredding."

Option C is the correct answer because it achieves this at a granular, field-level. BigQuery's AEAD (Authenticated Encryption with Associated Data) functions allow you to encrypt individual field values within your tables using a keyset from Cloud KMS. You can encrypt the sensitive data, store the encrypted value in BigQuery, and when an employee leaves, you simply destroy the specific KMS key version used for their data. Once the key is destroyed, the encrypted data is permanently unrecoverable, thus rendering it unreadable.

Option A and B are incorrect because data masking and column-level access controls are about controlling access to the data, not about making the underlying data itself unreadable. If an administrator with sufficient privileges were to query the table, the data would still be there in its original form. Revoking permissions prevents a specific user from seeing the data, but it doesn't erase it.

Option D is incorrect because Customer-Managed Encryption Keys (CMEK) operate at the entire table level. Deleting a CMEK key would make the entire table unreadable, which is far too broad and disruptive. The goal is to target specific sensitive values, not destroy whole tables.

Reference (Google Cloud Documentation Concepts):

This solution leverages field-level encryption within BigQuery. The official documentation for "Protecting data with Cloud KMS keys" states that BigQuery's AEAD functions (AEAD.ENCRYPT and AEAD.DECRYPT) are the intended mechanism for this purpose. This approach provides fine-grained control, allowing you to manage the lifecycle of encrypted data by managing the lifecycle of the encryption keys in Cloud Key Management Service (Cloud KMS). The act of destroying a key version is the mechanism for crypto-shredding.