Free Amazon Web Services Data-Engineer-Associate Practice Exam with Questions & Answers | Set: 3

Athena workgroups are a way to isolate query execution and query history among users, teams, and applications that share the same AWS account. By creating a workgroup for each use case, the company can control the access and actions on the workgroup resource using resource-level IAM permissions or identity-based IAM policies. The company can also use tags to organize and identify the workgroups, and use them as conditions in the IAM policies to grant or deny permissions to the workgroup. This solution meets the requirements of separating query processes and access to query history among users, teams, and applications that are in the same AWS account. References:

Athena Workgroups

IAM policies for accessing workgroups

Workgroup example policies

TheRedshift UNLOADcommand is specifically designed to export query results toAmazon S3, andAWS Gluecan orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:

“Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations.”

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

This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.

The telecommunications company needs a low-latency solution to detect sudden drops in network usage from real-time data collected throughout the day.

Option B: Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (Amazon Kinesis Data Analytics) application to detect drops in network usage.UsingAmazon KinesiswithManaged Service for Apache Flink (formerly Kinesis Data Analytics)is ideal for real-time stream processing with minimal latency. Flink can analyze the incoming data stream in real-time and detect anomalies, such as sudden drops in usage, which makes it the best fit for this scenario.

Other options (A, C, and D) either introduce unnecessary delays (e.g., querying databases) or do not provide the same real-time, low-latency processing that is critical for this use case.

Problem Analysis:

The company processes2 GB of daily sales recordsand100 GB of Salesforce sales opportunities.

The goal is to analyze and correlate the two datasets withlow operational overhead.

The process must runonce nightly.

Key Considerations:

Amazon AppFlowsimplifies data integration with Salesforce.

AWS Gluecan extract data from MySQL and perform ETL operations.

Step Functionscan orchestrate workflows with minimal manual intervention.

Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.

Solution Analysis:

Option A: MWAA + Lambda + Step Functions

Requires custom Lambda code for dataset correlation, increasing development and operational complexity.

Option B: AppFlow + Glue + MWAA

MWAA adds orchestration overhead compared to the simpler Step Functions.

Option C: AppFlow + Glue + Step Functions

AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.

Minimal setup and operational overhead, making it the best choice.

Option D: AppFlow + Kinesis + Flink + Step Functions

Using Kinesis and Flink for batch processing introduces unnecessary complexity.

Final Recommendation:

UseAmazon AppFlowto fetch Salesforce data,AWS Glueto process MySQL data, andStep Functionsfor orchestration.

Explanation: Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.

Step 1: Understand the Data Access Requirements

The question presents distinct access needs for three teams:

Marketing team: Needs full access to customer contact info but only obfuscated claim information.

Claims team: Needs access to customer information relevant to the claims they process.

Analytics team: Needs only obfuscated PII data.

These teams require different levels of access, and the solution needs to enforce data security while keeping administrative overhead low.

Step 2: Why Option B is Correct

Option B (Creating Views) is a common best practice in Amazon Redshift to restrict access to specific data without duplicating data or managing multiple clusters. By creating views:

You can define customized views of the data with obfuscated fields for the analytics team and marketing team while still providing full access where necessary.

Views provide a logical separation of data and allow Redshift administrators to grant access permissions based on roles or groups, ensuring that each team sees only what they are allowed to.

Obfuscation or masking of PII can be easily applied to the views by transforming or hiding sensitive data fields.

This approach avoids the complexity of managing multiple Redshift clusters or S3-based data lakes, which introduces higher operational and administrative overhead.

Step 3: Why Other Options Are Not Ideal

Option A (Separate Redshift Clusters) introduces unnecessary administrative overhead by managing multiple clusters. Maintaining several clusters for each team is costly, redundant, and inefficient.

Option C (Separate Redshift Roles) involves creating multiple roles and managing complex masking policies, which adds to administrative burden and complexity. While Redshift does support column-level access control, it's still more overhead than managing simple views.

Option D (Move to S3 and Lake Formation) is a more complex and heavy-handed solution, especially when the data is already stored in Redshift. Migrating the data to S3 and setting up a data lake with Lake Formation introduces significant operational complexity that isn't needed for this specific requirement.

Conclusion:

Creating views in Amazon Redshift allows for flexible, fine-grained access control with minimal overhead, making it the optimal solution to meet the data access requirements of the marketing, claims, and analytics teams.

The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use amanifest file.

Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.

Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.

Apache Parquet is a columnar storage format that is much more space-efficient than row-based formats like CSV, especially for analytics workloads. Transforming data from CSV to Parquet significantly reduces storage costs and improves query performance. According to the study guide:

“Parquet is a columnar storage file format that is optimized for use with analytics workloads, providing efficient storage and fast query performance.”

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

By switching to Parquet, the company can reduce both storage size and retrieval times, making it the optimal choice for cost-effective data analysis.

To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.

Option B: Run the ANALYZE COMPRESSION command against the identified tables. Manually update the compression encoding of columns based on the output of the command.TheANALYZE COMPRESSIONcommand in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.

Option A(ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.

Options C and D(VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.

The data engineer needs to detect custom categories of PII within the data lake usingAWS Glue DataBrew. While DataBrew provides standard data quality rules, the solution must support custom PII categories.

Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.This option is the most efficient because DataBrew allows the creation ofcustom data quality rulesthat can be applied to detect specific data patterns, includingcustom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.

Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.