Introduction
The AWS Certified Data Analytics – Specialty (DAS-C01) exam is intended for individuals who perform a data analytics role. The exam validates a candidate’s comprehensive understanding of how to use AWS services to design, build, secure, and maintain analytics solutions that provide insight from data.
The exam also validates a candidate’s ability to complete the following tasks:
Define AWS data analytics services and understand how they integrate with each other
Explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization
Target candidate description
The target candidate should have a minimum of 5 years of experience with common data analytics technologies. The target candidate also should have at least 2 years of hands-on experience and expertise working with AWS services to design, build, secure, and maintain analytics solutions.
What is considered out of scope for the target candidate?
The following is a non-exhaustive list of related job tasks that the target candidate is not expected to be able to perform. These items are considered out of scope for the exam:
Design and implement machine learning algorithms
Implement container-based solutions
Utilize high performance computing (HPC)
Design online transactional processing (OLTP) database solutions
For a detailed list of specific tools and technologies that might be covered on the exam, as well as lists of in-scope and out-of-scope AWS services, refer to the Appendix.
Exam content
Response types
There are two types of questions on the exam:
Multiple choice: Has one correct response and three incorrect responses (distractors)
Multiple response: Has two or more correct responses out of five or more response options
Select one or more responses that best complete the statement or answer the question. Distractors, or incorrect answers, are response options that a candidate with incomplete knowledge or skill might choose.
Distractors are generally plausible responses that match the content area.
Unanswered questions are scored as incorrect; there is no penalty for guessing. The exam includes
50 questions that will affect your score.
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Unscored content
The exam includes 15 unscored questions that do not affect your score. AWS collects information about candidate performance on these unscored questions to evaluate these questions for future use as scored questions. These unscored questions are not identified on the exam.
Exam results
The AWS Certified Data Analytics – Specialty (DAS-C01) exam is a pass or fail exam. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines.
Your results for the exam are reported as a scaled score of 100–1,000. The minimum passing score is 750.
Your score shows how you performed on the exam as a whole and whether or not you passed. Scaled scoring models help equate scores across multiple exam forms that might have slightly different difficulty levels.
Your score report could contain a table of classifications of your performance at each section level. This information is intended to provide general feedback about your exam performance. The exam uses a compensatory scoring model, which means that you do not need to achieve a passing score in each section. You need to pass only the overall exam.
Each section of the exam has a specific weighting, so some sections have more questions than other sections have. The table contains general information that highlights your strengths and weaknesses. Use caution when interpreting section-level feedback.
Content outline
This exam guide includes weightings, test domains, and objectives for the exam. It is not a comprehensive listing of the content on the exam. However, additional context for each of the objectives is available to help guide your preparation for the exam. The following table lists the main content domains and their weightings. The table precedes the complete exam content outline, which includes the additional context.
The percentage in each domain represents only scored content.
Domain % of Exam
Domain 5: Security 18%
TOTAL 100%
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Domain 1: Collection
1.1 Determine the operational characteristics of the collection system
Evaluate that the data loss is within tolerance limits in the event of failures
Evaluate costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (e.g., networking, bandwidth, ETL/data migration costs)
Assess the failure scenarios that the collection system may undergo, and take remediation actions based on impact
Determine data persistence at various points of data capture
Identify the latency characteristics of the collection system
1.2 Select a collection system that handles the frequency, volume, and the source of data
Describe and characterize the volume and flow characteristics of incoming data (streaming, transactional, batch)
Match flow characteristics of data to potential solutions
Assess the tradeoffs between various ingestion services taking into account scalability, cost, fault tolerance, latency, etc.
Explain the throughput capability of a variety of different types of data collection and identify bottlenecks
Choose a collection solution that satisfies connectivity constraints of the source data system
1.3 Select a collection system that addresses the key properties of data, such as order, format, and compression
Describe how to capture data changes at the source
Discuss data structure and format, compression applied, and encryption requirements
Distinguish the impact of out-of-order delivery of data, duplicate delivery of data, and the tradeoffs between at-most-once, exactly-once, and at-least-once processing
Describe how to transform and filter data during the collection process
Domain 2: Storage and Data Management
2.1 Determine the operational characteristics of the storage solution for analytics
Determine the appropriate storage service(s) on the basis of cost vs. performance
Understand the durability, reliability, and latency characteristics of the storage solution based on requirements
Determine the requirements of a system for strong vs. eventual consistency of the storage system
Determine the appropriate storage solution to address data freshness requirements
2.2 Determine data access and retrieval patterns
Determine the appropriate storage solution based on update patterns (e.g., bulk, transactional, micro batching)
Determine the appropriate storage solution based on access patterns (e.g., sequential vs.
random access, continuous usage vs.ad hoc)
Determine the appropriate storage solution to address change characteristics of data (appendonly changes vs. updates)
Determine the appropriate storage solution for long-term storage vs. transient storage
Determine the appropriate storage solution for structured vs. semi-structured data
Determine the appropriate storage solution to address query latency requirements
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2.3 Select appropriate data layout, schema, structure, and format
Determine appropriate mechanisms to address schema evolution requirements
Select the storage format for the task
Select the compression/encoding strategies for the chosen storage format
Select the data sorting and distribution strategies and the storage layout for efficient data access
Explain the cost and performance implications of different data distributions, layouts, and formats (e.g., size and number of files)
Implement data formatting and partitioning schemes for data-optimized analysis
2.4 Define data lifecycle based on usage patterns and business requirements
Determine the strategy to address data lifecycle requirements
Apply the lifecycle and data retention policies to different storage solutions
2.5 Determine the appropriate system for cataloging data and managing metadata
Evaluate mechanisms for discovery of new and updated data sources
Evaluate mechanisms for creating and updating data catalogs and metadata
Explain mechanisms for searching and retrieving data catalogs and metadata
Explain mechanisms for tagging and classifying data
Domain 3: Processing
3.1 Determine appropriate data processing solution requirements
Understand data preparation and usage requirements
Understand different types of data sources and targets
Evaluate performance and orchestration needs
Evaluate appropriate services for cost, scalability, and availability
3.2 Design a solution for transforming and preparing data for analysis
Apply appropriate ETL/ELT techniques for batch and real-time workloads
Implement failover, scaling, and replication mechanisms
Implement techniques to address concurrency needs
Implement techniques to improve cost-optimization efficiencies
Apply orchestration workflows
Aggregate and enrich data for downstream consumption
3.3 Automate and operationalize data processing solutions
Implement automated techniques for repeatable workflows
Apply methods to identify and recover from processing failures
Deploy logging and monitoring solutions to enable auditing and traceability
Domain 4: Analysis and Visualization
4.1 Determine the operational characteristics of the analysis and visualization solution
Determine costs associated with analysis and visualization
Determine scalability associated with analysis
Determine failover recovery and fault tolerance within the RPO/RTO
Determine the availability characteristics of an analysis tool
Evaluate dynamic, interactive, and static presentations of data
Translate performance requirements to an appropriate visualization approach (pre-compute and consume static data vs. consume dynamic data)
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4.2 Select the appropriate data analysis solution for a given scenario
Evaluate and compare analysis solutions
Select the right type of analysis based on the customer use case (streaming, interactive, collaborative, operational)
4.3 Select the appropriate data visualization solution for a given scenario
Evaluate output capabilities for a given analysis solution (metrics, KPIs, tabular, API)
Choose the appropriate method for data delivery (e.g., web, mobile, email, collaborative notebooks)
Choose and define the appropriate data refresh schedule
Choose appropriate tools for different data freshness requirements (e.g., Amazon Elasticsearch
Service vs. Amazon QuickSight vs. Amazon EMR notebooks)
Understand the capabilities of visualization tools for interactive use cases (e.g., drill down, drill through and pivot)
Implement the appropriate data access mechanism (e.g., in memory vs. direct access)
Implement an integrated solution from multiple heterogeneous data sources
Domain 5: Security
5.1 Select appropriate authentication and authorization mechanisms
Implement appropriate authentication methods (e.g., federated access, SSO, IAM)
Implement appropriate authorization methods (e.g., policies, ACL, table/column level permissions)
Implement appropriate access control mechanisms (e.g., security groups, role-based control)
5.2 Apply data protection and encryption techniques
Determine data encryption and masking needs
Apply different encryption approaches (server-side encryption, client-side encryption, AWS
KMS, AWS CloudHSM)
Implement at-rest and in-transit encryption mechanisms
Implement data obfuscation and masking techniques
Apply basic principles of key rotation and secrets management
5.3 Apply data governance and compliance controls
Determine data governance and compliance requirements
Understand and configure access and audit logging across data analytics services
Implement appropriate controls to meet compliance requirements
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Appendix
Which key tools, technologies, and concepts might be covered on the exam?
The following is a non-exhaustive list of the tools and technologies that could appear on the exam. This list is subject to change and is provided to help you understand the general scope of services, features, or technologies on the exam. AWS services are grouped according to their primary functions. While some of these technologies will likely be covered more than others on the exam, the order and placement of them in this list is no indication of relative weight or importance:
AWS services and features
Analytics:
Amazon Athena
Amazon CloudSearch
Amazon Elasticsearch Service (Amazon ES)
Amazon EMR
AWS Glue
Amazon Kinesis (excluding Kinesis Video Streams)
AWS Lake Formation
Amazon Managed Streaming for Apache Kafka
Amazon QuickSight
Amazon Redshift
Application Integration:
Amazon MQ
Amazon Simple Notification Service (Amazon SNS)
Amazon Simple Queue Service (Amazon SQS)
AWS Step Functions
Compute:
Amazon EC2
Elastic Load Balancing
AWS Lambda
Customer Engagement:
Amazon Simple Email Service (Amazon SES)
Database:
Amazon DocumentDB (with MongoDB compatibility)
Amazon DynamoDB
Amazon ElastiCache
Amazon Neptune
Amazon RDS
Amazon Redshift
Amazon Timestream
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Management and Governance:
AWS Auto Scaling
AWS CloudFormation
AWS CloudTrail
Amazon CloudWatch
AWS Trusted Advisor
Machine Learning:
Amazon SageMaker
Migration and Transfer:
AWS Database Migration Service (AWS DMS)
AWS DataSync
AWS Snowball
AWS Transfer for SFTP
Networking and Content Delivery:
Amazon API Gateway
AWS Direct Connect
Amazon VPC (and associated features)
Security, Identity, and Compliance:
AWS AppSync
AWS Artifact
AWS Certificate Manager (ACM)
AWS CloudHSM
Amazon Cognito
AWS Identity and Access Management (IAM)
AWS Key Management Service (AWS KMS)
Amazon Macie
AWS Secrets Manager
AWS Single Sign-On
Storage:
Amazon Elastic Block Store (Amazon EBS)
Amazon S3
Amazon S3 Glacier
Out-of-scope AWS services and features
The following is a non-exhaustive list of AWS services and features that are not covered on the exam.
These services and features do not represent every AWS offering that is excluded from the exam content.
Services or features that are entirely unrelated to the target job roles for the exam are excluded from this list because they are assumed to be irrelevant.
Out-of-scope AWS services and features include the following:
AWS IoT Core
Programme scope, delivery arrangements and suitability are confirmed during enquiry.