AWS DAS-C01 Free Practice Questions — Page 1

Correct Answer: C. Use Amazon Kinesis Data Firehose to push the data into an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster. Visualize the data by using an OpenSearch Dashboards (Kibana).

Why C is correct: Amazon Kinesis Data Firehose can directly push data into Amazon OpenSearch Service (formerly Elasticsearch Service), and OpenSearch Dashboards (formerly Kibana) provides native real-time visualization capabilities with automatic refresh intervals as low as 5 seconds. This is a fully managed solution requiring minimal implementation effort - you simply configure Firehose to deliver to OpenSearch and create dashboards in the OpenSearch Dashboards UI. Why others are wrong: A & D use QuickSight, which requires manual refresh and doesn't support automatic 5-second refresh intervals for streaming data B requires significant custom development effort with D3.js and managing EMR clusters, which violates the "least implementation effort" requirement

Correct Answer: A. AWS Lambda with a Python script

Why A is correct: AWS Lambda is the most cost-effective solution because it operates on a pay-per-execution model with no infrastructure costs when idle. With 1 GB memory requirement and couple-minute execution time per run, Lambda fits perfectly within its limits (up to 10 GB memory, 15 minutes timeout). Lambda natively supports Python, avoiding unnecessary conversion. Since data processing happens once per night per bed, Lambda's pricing model (charging only for actual compute time) is far more economical than continuously running clusters. Why others are wrong: B uses Scala instead of the existing Python script, requiring code conversion C EMR clusters are overkill for this simple processing task and incur costs even when idle D while AWS Glue supports PySpark, it's more expensive than Lambda for infrequent, short-duration jobs

Correct Answer: C. Heat map

Why C is correct: Heat maps excel at displaying two-dimensional data with color-coded values, making them ideal for visualizing revenue across two categorical dimensions (cities and stores). The color intensity naturally highlights outliers and trends, allowing the sales team to quickly identify high and low performers across the matrix of cities and stores without detailed analysis. Why others are wrong: A Geospatial charts show geographic locations but don't effectively display comparative metrics across categories B Line charts show trends over time, not comparisons across multiple categorical dimensions D Tree maps show hierarchical data and proportions but are less effective at identifying outliers compared to heat maps

Correct Answers: D. Use the groupFiles setting in the AWS Glue ETL job to merge small S3 files and rerun AWS Glue ETL jobs.; E. Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds

Why D is correct: The groupFiles setting in AWS Glue ETL jobs is specifically designed to address the small files problem by logically grouping multiple small files together during processing. This reduces memory pressure by processing files in batches rather than individually, directly addressing the OutOfMemoryError without additional infrastructure costs. Why E is correct: Increasing the Firehose buffer size to 128 MB and buffer interval to 900 seconds (15 minutes) creates larger files before writing to S3, addressing the root cause of the small files problem. Fewer, larger files reduce the number of objects the Glue job needs to process, preventing memory issues. This is a configuration change with no additional cost. Why others are wrong: A simply organizing files doesn't solve the processing issue B requires Lambda which adds operational overhead and doesn't avoid additional costs (Lambda execution costs) C requires running EMR which incurs significant additional costs

Correct Answer: B. Use QuickSight folders to organize dashboards, analyses, and datasets. Assign group permissions by using these folders.

Why B is correct: QuickSight folders provide organizational hierarchy for dashboards, analyses, and datasets, and most importantly, support group-level permissions. Assigning permissions to groups (rather than individual users) dramatically simplifies management at scale - you manage permissions once per group rather than thousands of times per user. This is the recommended AWS best practice for large-scale permissions management. Why others are wrong: A using individual user permissions defeats the purpose of simplification when dealing with thousands of users C IAM resource-based policies don't directly control QuickSight item access; QuickSight uses its own permission model D is overly complex and relies on naming conventions rather than using built-in permission features