AWS Certified Data Analytics Specialty Practice Exam
The AWS Certified Data Analytics Specialty certification, which is currently being retired, was designed to validate the knowledge and skills of individuals in designing, building, securing, and maintaining analytics solutions on the AWS platform.
Important Note: As of March 1, 2024, the AWS Certified Data Analytics Specialty exam is no longer available. It has been replaced by the AWS Certified Data Engineer - Associate exam. This new exam focuses more on the data engineering aspects like ingestion, transformation, storage, and management of data.
Who should have taken this Exam?
This certification was targeted towards individuals with experience and expertise in:
- Data analytics technologies (five years of experience)
- Designing, building, securing, and maintaining data analytics solutions on AWS (two years of experience)
- Understanding and integrating various AWS services for data analytics purposes
Key Responsibilities:
Individuals with this certification may have been involved in:
- Analyzing business requirements and translating them into technical solutions.
- Designing and implementing data pipelines using AWS services like S3, Redshift, Glue, etc.
- Developing and maintaining data processing and analytics applications.
- Monitoring and troubleshooting data pipelines and analytics infrastructure.
- Securing and managing access to data and resources on AWS.
Exam Details :
- Format: Multiple choice and multiple select questions
- Number of Questions: 65
- Duration: 180 minutes
- Passing Score: 750
Course Outline
The AWS Data Analytics Specialty Exam covers the following topics -
Module 1: Describe Collection (18%)
- Determining the operational characteristics of the collection system
- Selecting a collection system that handles the frequency, volume, and source of data
- Selecting a collection system that addresses the key properties of data, such as order, format, and compression
Module 2: Describe Storage and Data Management (22%)
- Determining the operational characteristics of a storage solution for analytics
- Determining data access and retrieval patterns
- Selecting suitable data layout, schema, structure, and format
- Defining a data lifecycle based on usage patterns and business requirements
- Determining a suitable system for cataloguing data and managing metadata
Module 3: Describe Processing (24%)
- Determining appropriate data processing solution requirements
- Designing a solution for transforming and preparing data for analysis
- Automating and operationalizing a data processing solution
Module 4: Analysis and Visualization (18%)
- Determining the operational characteristics of an analysis and visualization solution
- Selecting a suitable data analysis solution for a given scenario
- Selecting the appropriate data visualization solution for a given scenario
Module 5: Security (18%)
- 5.1 Selecting appropriate authentication and authorization mechanisms
- 5.2 Applying data protection and encryption methods
- 5.3 Applying data governance and compliance controls