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AWS Certified Data Analytics - Specialty Exam Intro
The AWS Certified Big Data - Specialty (BDS-C00) is designed for people performing complex Big Data analyzes. This exam validates a candidate's technical skills and experience in designing and implementing AWS services to achieve data value. Validate candidate's ability to: Implement AWS Big Data core services in accordance with best practices of basic architecture Design and manage Big Data leveraging tools to automate data analysis.
The Amazon Web Services (AWS) Certified Data Analytics – Specialty (DAS-C01) is an exam designed for individuals who are good at performing the data analytics role. It tests the knowledge of candidates in using AWS services to build, design, maintain, and secure analytics solutions. Those who apply for this test should understand the differences between various data analytics services and how they can fit in the data lifecycle, storage, collection, visualization, and processing.
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
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Amazon AWS-Certified-Data-Analytics-Specialty Exam Syllabus Topics:
| Section | Weight | Objectives |
| Processing | 24-28% | - Design batch and stream processing solutions
- Transform, clean, and prepare data for analysis
- Optimize processing performance and reliability
- Automate and operationalize data pipelines
|
| Analysis and Visualization | 18-22% | - Design and implement data visualization solutions
- Enable self-service analytics and reporting
- Optimize query performance and data access patterns
- Select appropriate analysis tools and query engines
|
| Security | 10-18% | - Ensure compliance, auditing, and monitoring
- Apply data governance and protection best practices
- Manage authentication, authorization, and access control
- Implement encryption at rest and in transit
|
| Storage and Data Management | 22-26% | - Implement data lifecycle policies and cost optimization
- Choose storage formats, schemas, and partitioning strategies
- Design data lake and data warehouse architectures
- Manage metadata and data cataloging
|
| Collection | 18-22% | - Address data properties: order, format, compression, frequency, volume
- Select appropriate ingestion services for batch and streaming data
- Determine operational characteristics of collection systems
|