Amazon DAS-C01 Korean : AWS Certified Data Analytics - Specialty (DAS-C01 Korean Version)

  • Exam Code: AWS-Certified-Data-Analytics-Specialty-KR
  • Exam Name: AWS Certified Data Analytics - Specialty (DAS-C01 Korean Version)
  • Updated: Jul 18, 2026
  • Q & A: 209 Questions and Answers

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How do you start to study for the AWS Certified Data Analytics Specialty Exam

Get the guide For AWS Certified Data Analytics Specialty Exam

Quick study guide if you don't have time to read complete the page

AWS offers the widest range of analysis tools and engines that analyze data using open formats and standards. To validate their experience with AWS data analysis solutions, manufacturers can now use the beta version of AWS Certified Data Analytics - Specialty Certification. AWS Certified Data Analytics: the specialized certification validates the technical experience in the design, creation, safety and maintenance of analysis solutions in AWS. This certification was first launched in 2017 as AWS - Specialty certified Big Data. The new name highlights the range of technical skills and experience in data and analysis validated by the certification. The new version of the exam includes updated content in all categories, from collection to viewing. It is the only AWS certification that specifically focuses on data analysis experience. This certification demonstrates its ability to design and implement analysis solutions that provide information through the visualization of data with appropriate security and automation measures.

Preparation Process

The most critical part of any IT certification exam is thorough preparation. These are the steps you can rely on when studying for your Amazon AWS Certified Data Analytics – Specialty test. In a very visual way, the AWS training allows you to fully understand every important component of this exam.

  • Exam Blueprint

    The first step to preparing for the Amazon DAS-C01 test is to visit the official webpage, check out what the exam is about, and find out what preparation resources are available & recommended. You will find the official study guide, which contains all the exam objectives in detail, as well as recommendations for further study.

  • AWS Whitepapers

    Any preparation process for the Amazon exam should not be complete without the use of the AWS whitepapers. The vendor has numerous documents that directly refer to the Data Analytics products. One of the most recommended papers that you will find in the AWS Data Analytics Learning Path is Kinesis.

  • Practice Tests

    The next step should be going through the free sample questions that are available for the applicants on the official website. These questions are important as they help you imagine how and what the real exam will look like. It will also allow you to easily assess the knowledge you have acquired through the use of guides, books, whitepapers, and so on.

  • AWS Training Courses

    To prepare for your certification exam, you can take and complete the AWS training, which is Exam Readiness: AWS Certified Data Analytics – Specialty. This is a free course that lasts for 3 ½ hours. Through this training, you will be able to prepare for the test by exploring its subject areas and familiarizing yourself with the exam approach and question style.

Reference: https://d1.awsstatic.com/training-and-certification/docs-data-analytics-specialty/AWS-Certified-Data-Analytics-Specialty-Exam-Guide_v1.0_08-23-2019_FINAL.pdf

Amazon DAS-C01 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Security18%- Implement data security
  • 1. Access control and IAM
  • 2. Encryption at rest and in transit
  • 3. Audit and compliance
- Secure analytics infrastructure
Topic 2: Collection18%- Optimize ingestion performance and cost
- Design data ingestion solutions
  • 1. Batch data collection
  • 2. Integration with external sources
  • 3. Streaming data collection
  • 4. Hybrid data ingestion
Topic 3: Analysis and Visualization18%- Perform data analysis
  • 1. Complex analytics
  • 2. Ad-hoc querying
  • 3. Reporting and dashboarding
- Enable self-service analytics
Topic 4: Storage and Data Management22%- Design data storage solutions
  • 1. Data warehouse design
  • 2. Data lifecycle management
  • 3. Data lake architecture
- Manage data access and organization
Topic 5: Processing24%- Optimize processing performance and reliability
- Design and build data processing solutions
  • 1. ETL/ELT workflows
  • 2. Stream processing
  • 3. Batch processing

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