Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

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

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

“RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.

Problem Analysis:

Input Requirements: Gaming app generates approximately 20 KB data records, which must be ingested and made available to three internal consumers with dedicated throughput.

Key Requirements:

High throughput for ingestion from each device.

Dedicated processing bandwidth for each consumer.

Key Considerations:

Amazon Kinesis Data Streams supports high-throughput ingestion with PutRecords API for batch writes.

The Enhanced Fan-Out feature provides dedicated throughput to each consumer, avoiding bandwidth contention.

This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.

Solution Analysis:

Option A: Kinesis Data Streams + Enhanced Fan-Out

PutRecords API is designed for batch writes, improving ingestion performance.

Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.

Option B: Data Firehose + Dedicated Throughput Request

Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.

Option C: Data Firehose + Enhanced Fan-Out

Firehose does not support enhanced fan-out. This option is invalid.

Option D: Kinesis Data Streams + EC2 Instances

Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.

Final Recommendation:

Use Kinesis Data Streams with Enhanced Fan-Out for high-throughput ingestion and dedicated consumer bandwidth.

Kinesis Data Streams Enhanced Fan-Out

PutRecords API for Batch Writes