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DAS-C01 Real Exam Questions

AWS Certified Data Analytics - Specialty (DAS-C01) Exam. Everything you need to prepare, practice, and pass.

190

Questions

5

Exam Domains

Included

Explanations

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Certification Overview

The exam emphasizes streaming and batch data ingestion (Kinesis, Firehose), data storage and management (S3, Redshift, Athena, Glue Data Catalog), ETL/processing workflows (AWS Glue), and visualization (QuickSight). Security, cost optimization, and data partitioning are tested across all domains.

What This Certification Proves

The AWS Certified Data Analytics - Specialty validates expertise in collecting, storing, processing, and analyzing data using AWS services. This certification demonstrates the ability to design and implement scalable analytics solutions, making it essential for professionals building modern data platforms on AWS.

Who Should Take This Exam

AWS Solutions Architects, data engineers, and analytics professionals with 2+ years of AWS experience and hands-on knowledge of data analytics services. Ideal for those transitioning into specialized data roles or advancing from associate-level certifications.

Topic Breakdown

5 domains covering 190 questions

DomainQuestionsWeight
Processing6836%
Storage And Data Management4725%
Security3016%
Analysis And Visualization2614%
Collection1910%

Study Plans

Choose a study plan that matches your schedule and experience level

30 Days

Intensive Sprint

Week 1-2

  • Master fundamentals: Processing
  • Read Amazon official documentation
  • Complete 7 questions daily

Week 3

  • Deep dive: Storage And Data Management
  • Review weak areas from results
  • Take 2 full-length exams

Week 4

  • Review all flagged questions
  • Timed exams to build stamina
  • Final revision of key concepts

60 Days

Balanced Approach

Week 1-2

  • Survey all exam domains
  • Set up study environment
  • Begin with foundational topics

Week 3-4

  • Focus: Processing
  • Focus: Storage And Data Management
  • 4 questions daily

Week 5-6

  • Focus: Security
  • Hands-on labs if applicable
  • Review explanations for wrong answers

Week 7-8

  • Complete all 190 questions
  • Identify and eliminate weak areas
  • Take 3 full-length timed tests

90 Days

Comprehensive Study

Month 1

  • Learn all exam domains at a comfortable pace
  • Build strong foundational knowledge
  • 3 questions daily

Month 2

  • Deep dive into each domain
  • Hands-on practice and labs
  • Take weekly timed exams

Month 3

  • Work through all 190 questions
  • Identify and eliminate weak areas
  • Take 3 full-length timed exams

DAS-C01-Specific Tips

  • Master Kinesis Streams vs Firehose trade-offs—streaming is a major domain and exam heavily tests use-case selection
  • Build a hands-on lab: ingest data with Kinesis → transform with Glue → query with Athena → visualize in QuickSight
  • Deep dive into Redshift cluster design, distribution keys, sort keys, and cost optimization—many questions focus on performance tuning
  • Understand AWS Glue Data Catalog schema management and partitioning strategies; the exam tests data organization patterns heavily
  • Practice QuickSight dashboard design—SPICE caching, row-level security, and visualization best practices appear frequently
  • Study data security patterns: encryption at rest/transit, IAM policies for analytics services, and cross-account access
  • Review data partitioning strategies (S3 partitioning, Athena partition projection) as they impact both performance and cost

Relevant Career Roles

Data Analytics EngineerAWS Data EngineerAnalytics Solutions ArchitectData Warehouse SpecialistBusiness Intelligence Engineer

Sample Questions

Try 5 free questions from the DAS-C01 question bank

Q1Analysis and Visualization

A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system. Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

Q2Processing

A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs. What is an explanation for this behavior and what is the solution?

Q3Collection

A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB. How should a data analytics specialist design the solution for data ingestion?

Q4Processing

A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream. After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically. What should the data analyst do to resolve this?

Q5Security

A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (PII) Only authorized users should be able to see plaintext PII data. What is the MOST operationally efficient solution that meets these requirements?

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