Exam Name: AWS Certified Data Analytics - Specialty (DAS-C01) Exam
Certification Provider: Amazon
Corresponding Certification: AWS Certified Data Analytics
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Difficulty in Writing AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam
The AWS Certified Data Analytics - Specialty (DAS-C01) test is a pass or fail exam. The examination is scored based on a set standard built by AWS experts who are motivated by certification industry's most reliable practices and guidelines.
The AWS Certified Cloud Practitioner 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 700. 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 may 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 others. The table contains general information that highlights your strengths and weaknesses. Use caution when interpreting section-level feedback. Passing candidates will not receive this additional information.
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AWS Data Analytics Specialty Exam Syllabus Topics:
| Section | Objectives |
|---|---|
Collection - 18% | |
| 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 |
| 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 |
| 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 |
Storage and Data Management - 22% | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
Processing - 24% | |
| 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 |
| 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 |
| 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 |
Analysis and Visualization - 18% | |
| 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) |
| 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) |
| 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 |
Security - 18% | |
| 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) |
| 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 |
| 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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