Specialty

AWS Certified Data Analytics - Specialty (DAS-C01) Practice Exam

The DAS-C01 exam validates expertise in designing and operating data analytics solutions on AWS using Kinesis, EMR, Glue, Athena, Redshift, QuickSight, and Lake Formation.

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DAS-C01 Exam Overview

Exam CodeDAS-C01
Full NameAWS Certified Data Analytics - Specialty
LevelSpecialty
Questions on Exam65
Duration180 minutes
Passing Score750 / 1000
Exam Cost$300 USD
Recommended Study Time60–100 hours
AWSReady Practice Questions100+

Exam Domains

Sample DAS-C01 Practice Questions

Try these free practice questions. Full answers and explanations are included.

Question 1

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near- real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.Which solution meets these requirements?

A. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
B. Use Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a custom application for the dashboard by using D3.js.
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).
D. Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard
Show Answer & Explanation

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

Question 2

An IoT company wants to release a new device that will collect data to track sleep overnight on an intelligent mattress. Sensors will send data that will be uploaded to an Amazon S3 bucket. About 2 MB of data is generated each night for each bed. Data must be processed and summarized for each user, and the results need to be available as soon as possible. Part of the process consists of time windowing and other functions. Based on tests with a Python script, every run will require about 1 GB of memory and will complete within a couple of minutes.Which solution will run the script in the MOST cost-effective way?

A. AWS Lambda with a Python script
B. AWS Glue with a Scala job
C. Amazon EMR with an Apache Spark script
D. AWS Glue with a PySpark job
Show Answer & Explanation

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

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Frequently Asked Questions — DAS-C01 Exam

How many questions are on the AWS DAS-C01 exam?
The DAS-C01 exam contains 65 questions to be completed in 180 minutes.
What is the passing score for DAS-C01?
The AWS Certified Data Analytics Specialty (DAS-C01) passing score is 750 out of 1000.
Is DAS-C01 hard?
DAS-C01 is moderately challenging. It requires broad knowledge of the AWS data analytics ecosystem and the ability to design end-to-end data pipelines.
How long should I study for DAS-C01?
Plan on 6–10 weeks studying 1–2 hours per day. Focus on Kinesis, EMR, Glue, Athena, Redshift, QuickSight, Lake Formation, and data security.
What topics does DAS-C01 cover?
DAS-C01 covers Kinesis Data Streams/Firehose/Analytics, EMR, Glue, Athena, Redshift, QuickSight, Lake Formation, S3 data lake patterns, and data security/governance.
Do I need a data engineering background for DAS-C01?
AWS recommends 5+ years of experience in a data-related role. Experience with big data technologies and data pipeline design is strongly recommended.
Are AWSReady DAS-C01 questions representative of the real exam?
Yes. AWSReady DAS-C01 questions cover all five exam domains including data ingestion, processing, analytics, and security scenarios.

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