Amazon-DEA-C01 Exam Questions

Problem Analysis:

The goal is to create a new empty table in Athena with the same schema as an existing table (old_table).

The solution must avoid copying any data.

Key Considerations:

CREATE TABLE AS (CTAS) is commonly used in Athena for creating new tables based on an existing table.

Adding the WITH NO DATA clause ensures only the schema is copied, without transferring any data.

Solution Analysis:

Option A: Copies both schema and data. Does not meet the requirement for an empty table.

Option B: Inserts data into an existing table, which does not create a new table.

Option C: Creates an empty table but does not copy the schema.

Option D: Creates a new table with the same schema and ensures it is empty by using WITH NO DATA.

Final Recommendation:

Use D. CREATE TABLE new_table AS (SELECT * FROM old_table) WITH NO DATA to create an empty table with the same schema.

Athena CTAS Queries

CREATE TABLE Statement in Athena

The best solution for managing SSL/TLS certificates on EC2 instances with minimal operational overhead is to use AWS Certificate Manager (ACM). ACM simplifies certificate management by automating the provisioning, renewal, and deployment of certificates.

AWS Certificate Manager (ACM):

ACM manages SSL/TLS certificates for EC2 and other AWS resources, including automatic certificate renewal. This reduces the need for manual management and avoids operational complexity.

ACM also integrates with other AWS services to simplify secure connections between AWS infrastructure and customer-managed environments.

Alternatives Considered:

A (Self-managed certificates): Managing certificates manually on EC2 instances increases operational overhead and lacks automatic renewal.

C (Secrets Manager automation): While Secrets Manager can store keys and certificates, it requires custom automation for rotation and does not handle SSL/TLS certificates directly.

D (ECS Service Connect): This is unrelated to SSL/TLS certificate management and would not address the operational need.

AWS Certificate Manager Documentation

AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can create data quality checks to ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger an Amazon EventBridge event that sends a message to an SNS topic.

AWS Glue Workflows:

AWS Glue workflows allow users to automate and monitor complex ETL processes. You can include data quality actions to check for null values, data types, and other consistency checks.

In the event of incomplete data, an EventBridge event can be generated to notify via SNS.

Alternatives Considered:

A (Airflow cluster): Managed Airflow introduces more operational overhead and complexity compared to Glue workflows.

B (EMR cluster): Setting up an EMR cluster is also more complex compared to the Glue-centric solution.

D (Lambda functions): While Lambda functions can work, using Glue workflows offers a more integrated and lower operational overhead solution.

AWS Glue Workflow Documentation

To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.

A . AWS Glue DataBrew for Masking:

AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.

D . AWS Identity and Access Management (IAM):

Using IAM policies allows fine-grained control over access to PII data, ensuring that only authorized users can view or process sensitive data during the pipeline stages.

Alternatives Considered:

B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data masking or access control for PII.

C (Amazon Macie): Macie can help discover sensitive data but does not handle the masking of PII or access control.

E (Custom scripts): Custom scripting increases the operational burden compared to a built-in solution like DataBrew.

AWS Glue DataBrew for Data Masking

IAM Policies for PII Access Control

To reclaim the most storage space from a materialized view in Amazon Redshift, you should use a DELETE operation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as 1=1. This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.

Option A: DELETE FROM materialized_view_name WHERE 1=1; This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.

Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don't remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).

Amazon Redshift Materialized Views Documentation

Deleting Data from Redshift