Amazon-DEA-C01 AWS Certified Data Engineer - Associate Exam Practice Test

Answer : D

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

Answer : A

Problem Analysis:

The company must log all user activities and connection activities in Amazon Redshift for security compliance.

Key Considerations:

Redshift supports audit logging, which can be configured to write logs to an S3 bucket.

S3 provides durable, scalable, and cost-effective storage for logs.

Solution Analysis:

Option A: S3 for Logging

Standard approach for storing Redshift logs.

Easy to set up and manage with minimal cost.

Option B: Amazon EFS

EFS is unnecessary for this use case and less cost-efficient than S3.

Option C: Aurora MySQL

Using a database to store logs increases complexity and cost.

Option D: EBS Volume

EBS is not a scalable option for log storage compared to S3.

Final Recommendation:

Enable Redshift audit logging and specify an S3 bucket as the destination.

Amazon Redshift Audit Logging

Storing Logs in Amazon S3

Answer : B

Problem Analysis:

The company needs near real-time replication of MySQL updates to Amazon Redshift.

Minimal development effort is required for this solution.

Key Considerations:

AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.

DMS integrates natively with both MySQL and Redshift, simplifying setup.

Solution Analysis:

Option A: AWS Glue Job

Glue is batch-oriented and does not support near real-time replication.

Option B: DMS with Full Load + CDC

Efficiently handles initial database load and continuous updates.

Requires minimal setup and operational overhead.

Option C: AppFlow SDK

AppFlow is not designed for database replication. Custom connectors increase development effort.

Option D: DataSync

DataSync is for file synchronization and not suitable for database updates.

Final Recommendation:

Use AWS DMS in full load + CDC mode for continuous replication.

AWS Database Migration Service Documentation

Setting Up DMS with Redshift

Answer : B

Problem Analysis:

The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.

Data transmission delays are acceptable, but the solution must maximize throughput efficiency.

Key Considerations:

The Kinesis Producer Library (KPL) batches records and uses aggregation to optimize shard throughput.

Efficiently handles high-throughput scenarios with minimal operational overhead.

Solution Analysis:

Option A: Kinesis Agent

Designed for file-based ingestion; not optimized for geolocation records.

Option B: KPL

Aggregates records into larger payloads, significantly improving shard throughput.

Suitable for applications generating small, high-frequency records.

Option C: Kinesis Firehose

Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.

Option D: Kinesis SDK

The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.

Final Recommendation:

Use Kinesis Producer Library (KPL) for its built-in aggregation and batching capabilities.

Kinesis Producer Library (KPL) Overview

Answer : B

Problem Analysis:

Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.

Processing time is increasing due to the additional testing facilities.

The goal is to reduce processing time while using the existing AWS Glue framework.

Key Considerations:

AWS Glue offers the dynamic frame file-grouping feature, which consolidates small files into larger, more efficient datasets during processing.

Grouping smaller files reduces overhead and speeds up processing.

Solution Analysis:

Option A: Lambda for File Grouping

Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.

Option B: AWS Glue Dynamic Frame File-Grouping

This option directly addresses the issue by grouping small files during Glue job execution.

Minimizes data processing time with no extra overhead.

Option C: Redshift COPY Command

COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).

Option D: Amazon EMR

While EMR is powerful, replacing Glue with EMR increases operational complexity.

Final Recommendation:

Use AWS Glue dynamic frame file-grouping for optimized data ingestion and processing.

AWS Glue Dynamic Frames

Optimizing Glue Performance

Answer : A

Step 1: Understanding the Problem

The company collects clickstream data via Amazon Kinesis Data Streams and stores it in JSON format in Amazon S3 using Kinesis Data Firehose. They use Amazon Athena to query the data, but they want to reduce Athena costs while maintaining the same data pipeline.

Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format like Apache Parquet) is a key solution to lowering costs.

Step 2: Why Option A is Correct

Option A provides a straightforward way to reduce costs with minimal management overhead:

Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.

Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.

AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one-time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.

ALTER TABLE ADD PARTITION: This command updates Athena's table metadata to reflect the new partitions, ensuring that future queries target only the required data.

Step 3: Why Other Options Are Not Ideal

Option B (Apache Spark on EMR) introduces higher management effort by requiring the setup of Apache Spark jobs and an Amazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.

Option C (Kinesis and Apache Flink) is a more complex solution involving Apache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.

Option D (AWS Lambda with Firehose) suggests using AWS Lambda to convert records in real time. While Lambda can work in some cases, it's generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations. Additionally, running parallel Glue jobs further complicates the setup.

Step 4: How Option A Minimizes Costs

By using Apache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.

Firehose natively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.

The AWS Glue ETL job for historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.

Conclusion:

Option A meets the requirement to reduce Athena costs without recreating the data pipeline, using Firehose's native support for Apache Parquet and a simple one-time AWS Glue ETL job for existing data. This approach involves minimal management effort compared to the other solutions.

Answer : D

Step 1: Understanding the Data Use Case

The company has data stored in an Amazon S3 bucket and needs to provide teams access for analysis, ensuring that PII data is not included in the analysis. The solution should be simple to implement and maintain, ensuring minimal operational overhead.

Step 2: Why Option D is Correct

Option D (AWS Glue DataBrew) allows you to visually prepare and transform data without needing to write code. By using a DataBrew job, the company can:

Automatically detect and separate PII data from non-PII data.

Store PII data in a second S3 bucket for security, while keeping the original S3 bucket clean for analysis.

This approach keeps operational overhead low by utilizing DataBrew's pre-built transformations and the easy-to-use interface for non-technical users. It also ensures compliance by separating sensitive PII data from the main dataset.

Step 3: Why Other Options Are Not Ideal

Option A (Amazon Macie) is a powerful tool for detecting sensitive data, but Macie doesn't inherently remove or mask PII. You would still need additional steps to clean the data after Macie identifies PII.

Option B (S3 Object Lambda with Amazon Comprehend) introduces more complexity by requiring custom logic at the point of data access. Amazon Comprehend can detect PII, but using S3 Object Lambda to filter data would involve more overhead.

Option C (Kinesis Data Firehose and Comprehend) is more suitable for real-time streaming data use cases rather than batch analysis. Setting up and managing a streaming solution like Kinesis adds unnecessary complexity.

Conclusion:

Using AWS Glue DataBrew provides a low-overhead, no-code solution to detect and separate PII data, ensuring the analysis teams only have access to non-sensitive data. This approach is simple, compliant, and easy to manage compared to other options.