Every organization today are hiring data analysts to handle the growing amount of data generated and collect. Data analysts are primarily responsible for handling data and spot trends, as well as making forecasts, and extracting information to help employers make better business decisions. Moreover, the role of the data analyst has become all the more important with employment opportunities in industries ranging from finance to marketing to social media. In this article, we are going to help you prepare for AWS Certified Data Analytics Specialty Exam with the latest learning resources.
The AWS Certified Data Analytics Specialty Exam credential assists organizations recognize and promoting talent with important skills for performing cloud initiatives. Obtaining an AWS Certified Data Analytics – Specialty substantiates expertise in working the AWS data lakes and analytics services to get perspicacity from data. Further, the exam evaluates the knowledge and comprehension of candidates to create, build, operationalize and defend the data analytics solutions on the AWS platform. Additionally, it examines the candidate’s expression to several data analytics-related services given by AWS.
It’s time to look at some required knowledge and understanding!
Recommended Knowledge
AWS Certified Data Analytics – Specialty is designed for people with expertise operating with AWS services to build, design, secure, and manage analytics solutions. Before you take this exam, we suggest you have:
- First of all, 5 or more years of expertise with data analytics technologies
- Also, the candidate must have minimum of 2 years of hands-on practrice working on the AWS
- Lastly, knowledge of working with AWS services for building, designing, securing, and sustaining analytics solutions.
Exam Specifications
Getting the exam construction format goes a long approach in the preparations. It serves as a draft for the exam and encourages the candidate to qualify for what they will encounter on the exam day. Familiarising with the exam format will support the candidate adjust their preparations with the exam and its purposes.
To begin with, the applicant will have to strive for 65 Multiple Choice and Multi-Response Questions. Also, the candidate will get 180 minutes to complete it. Moreover, it will cost them approximately $300 USD and is available in English, Korean, Japanese, and Simplified Chinese. languages. Most significantly, they need to get passing marks of 75%-80% to obtain this credential.
Now comes the most important part, the course outline of the AWS exam.
Course Structure
The applicant should understand the fields and their concerned topics.
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 (AWS Documentation: Fault tolerance, Failure Management)
- Evaluate costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (e.g., networking, bandwidth, ETL/data migration costs) (AWS Documentation: Cloud Data Migration, Plan for Data Transfer, Amazon EC2 FAQs)
- Assess the failure scenarios that the collection system may undergo, and take remediation actions based on impact (AWS Documentation: Remediating Noncompliant AWS Resources, CIS AWS Foundations Benchmark controls, Failure Management)
- Determine data persistence at various points of data capture (AWS Documentation: Capture data)
- Identify the latency characteristics of the collection system (AWS Documentation: I/O characteristics and monitoring, Amazon CloudWatch concepts)
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) (AWS Documentation: Characteristics, streaming data)
- Match flow characteristics of data to potential solutions
- Assess the tradeoffs between various ingestion services taking into account scalability, cost, fault tolerance, latency, etc. (AWS Documentation: Amazon EMR FAQs, Data ingestion methods)
- Explain the throughput capability of a variety of different types of data collection and identify bottlenecks (AWS Documentation: Caching Overview, I/O characteristics and monitoring)
- 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 (AWS Documentation: Capture changes from Amazon DocumentDB, Creating tasks for ongoing replication using AWS DMS, Using change data capture)
- Discuss data structure and format, compression applied, and encryption requirements (AWS Documentation: Compression encodings, Athena compression support)
- 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 (AWS Documentation: Amazon SQS FIFO (First-In-First-Out) queues, Amazon Simple Queue Service)
- Describe how to transform and filter data during the collection process (AWS Documentation: Transform Data, Filter class)
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 (AWS Documentation: Amazon S3 pricing, Storage Architecture Selection)
- Understand the durability, reliability, and latency characteristics of the storage solution based on requirements (AWS Documentation: Storage, Selection)
- Determine the requirements of a system for strong vs. eventual consistency of the storage system (AWS Documentation: Amazon S3 Strong Consistency, Consistency Model)
- Determine the appropriate storage solution to address data freshness requirements (AWS Documentation: Storage, Storage Architecture Selection)
2.2 Determine data access and retrieval patterns
- Determine the appropriate storage solution based on update patterns (e.g., bulk, transactional, micro batching) (AWS Documentation: select your storage solution, Performing large-scale batch operations, Batch data processing)
- Determine the appropriate storage solution based on access patterns (e.g., sequential vs. random access, continuous usage vs.ad hoc) (AWS Documentation: optimizing Amazon S3 performance, Amazon S3 FAQs)
- 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 (AWS Documentation: Storage, Using Amazon S3 storage classes)
- Determine the appropriate storage solution for structured vs. semi-structured data (AWS Documentation: Ingesting and querying semistructured data in Amazon Redshift, Storage Best Practices for Data and Analytics Applications)
- Determine the appropriate storage solution to address query latency requirements (AWS Documentation: In-place querying, Performance Guidelines for Amazon S3, Storage Architecture Selection)
2.3 Select appropriate data layout, schema, structure, and format
- Determine appropriate mechanisms to address schema evolution requirements (AWS Documentation: Handling schema updates, Best practices for securing sensitive data in AWS data stores)
- Select the storage format for the task (AWS Documentation: Task definition parameters, Specifying task settings for AWS Database Migration Service tasks)
- Select the compression/encoding strategies for the chosen storage format (AWS Documentation: Choosing compression encodings for the CUSTOMER table, Compression encodings)
- Select the data sorting and distribution strategies and the storage layout for efficient data access (AWS Documentation: Best practices for using sort keys to organize data, Working with data distribution styles)
- Explain the cost and performance implications of different data distributions, layouts, and formats (e.g., size and number of files) (AWS Documentation: optimizing Amazon S3 performance)
- Implement data formatting and partitioning schemes for data-optimized analysis (AWS Documentation: Partitioning data in Athena, Partitions and data distribution)
2.4 Define data lifecycle based on usage patterns and business requirements
- Determine the strategy to address data lifecycle requirements (AWS Documentation: Amazon Data Lifecycle Manager)
- Apply the lifecycle and data retention policies to different storage solutions (AWS Documentation: Setting lifecycle configuration on a bucket, Managing your storage lifecycle)
2.5 Determine the appropriate system for cataloging data and managing metadata
- Evaluate mechanisms for discovery of new and updated data sources (AWS Documentation: Discovering on-premises resources using AWS discovery tools)
- Evaluate mechanisms for creating and updating data catalogs and metadata (AWS Documentation: Catalog and search, Data cataloging)
- Explain mechanisms for searching and retrieving data catalogs and metadata (AWS Documentation: Understanding tables, databases, and the Data Catalog)
- Explain mechanisms for tagging and classifying data (AWS Documentation: Data Classification, Data classification overview)
Domain 3: Processing
3.1 Determine appropriate data processing solution requirements
- Understand data preparation and usage requirements (AWS Documentation: Data Preparation, Preparing data in Amazon QuickSight)
- Understand different types of data sources and targets (AWS Documentation: Targets for data migration, Sources for data migration)
- Evaluate performance and orchestration needs (AWS Documentation: Performance Efficiency)
- Evaluate appropriate services for cost, scalability, and availability (AWS Documentation: High availability and scalability on AWS)
3.2 Design a solution for transforming and preparing data for analysis
- Apply appropriate ETL/ELT techniques for batch and real-time workloads (AWS Documentation: ETL and ELT design patterns for lake house architecture)
- Implement failover, scaling, and replication mechanisms (AWS Documentation: Disaster recovery options in the cloud, Working with read replicas)
- Implement techniques to address concurrency needs (AWS Documentation: Managing Lambda reserved concurrency, Managing Lambda provisioned concurrency)
- Implement techniques to improve cost-optimization efficiencies (AWS Documentation: Cost Optimization)
- Apply orchestration workflows (AWS Documentation: AWS Step Functions)
- Aggregate and enrich data for downstream consumption (AWS Documentation: Joining and Enriching Streaming Data on Amazon Kinesis, Designing a High-volume Streaming Data Ingestion Platform Natively on AWS)
3.3 Automate and operationalize data processing solutions
- Implement automated techniques for repeatable workflows
- Apply methods to identify and recover from processing failures (AWS Documentation: Failure Management, Recover your instance)
- Deploy logging and monitoring solutions to enable auditing and traceability (AWS Documentation: 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 (AWS Documentation: Analyzing your costs with AWS Cost Explorer)
- Determine scalability associated with analysis (AWS Documentation: Predictive scaling for Amazon EC2 Auto Scaling)
- Determine failover recovery and fault tolerance within the RPO/RTO (AWS Documentation: Plan for Disaster Recovery (DR))
- Determine the availability characteristics of an analysis tool (AWS Documentation: Analytics)
- Evaluate dynamic, interactive, and static presentations of data (AWS Documentation: Data Visualization, Use static and dynamic device hierarchies)
- Translate performance requirements to an appropriate visualization approach (pre-compute and consume static data vs. consume dynamic data)
4.2 Select the appropriate data analysis solution for a given scenario
- Evaluate and compare analysis solutions (AWS Documentation: Evaluating a solution version with metrics)
- 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) (AWS Documentation: Using KPIs, Using Amazon CloudWatch metrics)
- Choose the appropriate method for data delivery (e.g., web, mobile, email, collaborative notebooks) (AWS Documentation: Amazon SageMaker Studio Notebooks architecture, Ensure efficient compute resources on Amazon SageMaker)
- Choose and define the appropriate data refresh schedule (AWS Documentation: Refreshing SPICE data, Refreshing data in Amazon QuickSight)
- Choose appropriate tools for different data freshness requirements (e.g., Amazon Elasticsearch Service vs. Amazon QuickSight vs. Amazon EMR notebooks) (AWS Documentation: Amazon EMR, Choosing the hardware for your Amazon EMR cluster, Build an automatic data profiling and reporting solution)
- Understand the capabilities of visualization tools for interactive use cases (e.g., drill down, drill through, and pivot) (AWS Documentation: Adding drill-downs to visual data in Amazon QuickSight, Using pivot tables)
- Implement the appropriate data access mechanism (e.g., in memory vs. direct access) (AWS Documentation: Security Best Practices for Amazon S3, Identity and access management in Amazon S3)
- Implement an integrated solution from multiple heterogeneous data sources (AWS Documentation: Data Sources and Ingestion)
Domain 5: Security
5.1 Select appropriate authentication and authorization mechanisms
- Implement appropriate authentication methods (e.g., federated access, SSO, IAM) (AWS Documentation: Identity and access management for IAM Identity Center)
- Implement appropriate authorization methods (e.g., policies, ACL, table/column level permissions) (AWS Documentation: Managing access permissions for AWS Glue resources, Policies and permissions in IAM)
- Implement appropriate access control mechanisms (e.g., security groups, role-based control) (AWS Documentation: Implement access control mechanisms)
5.2 Apply data protection and encryption techniques
- Determine data encryption and masking needs (AWS Documentation: Protecting Data at Rest, Protecting data using client-side encryption)
- Apply different encryption approaches (server-side encryption, client-side encryption, AWS KMS, AWS CloudHSM) (AWS Documentation: AWS Key Management Service FAQs, Protecting data using server-side encryption, Cryptography concepts)
- Implement at-rest and in-transit encryption mechanisms (AWS Documentation: Encrypting Data-at-Rest and -in-Transit)
- Implement data obfuscation and masking techniques (AWS Documentation: Data masking using AWS DMS, Create a secure data lake by masking)
- Apply basic principles of key rotation and secrets management (AWS Documentation: Rotate AWS Secrets Manager secrets)
5.3 Apply data governance and compliance controls
- Determine data governance and compliance requirements (AWS Documentation: Management and Governance)
- Understand and configure access and audit logging across data analytics services (AWS Documentation: Monitoring audit logs in Amazon OpenSearch Service)
- Implement appropriate controls to meet compliance requirements (AWS Documentation: Security and compliance)
Learning Resources to Refer!
- Exploring AWS Exam Guide– This learning path or Exam Guide is intended for individuals who perform a data analytics role. Majorly for those who wants perform a role involving AWS Certified Data Analytics Specialty. Also, anyone with beginner-level experience who wants to learn to design, build, secure, and maintain analytics solutions that provide insight from data can refer to this.
- Testprep Online Tutorials– AWS Certified Data Analytics Specialty (DAS-C01) Online Tutorial enhances the knowledge and provides a depth knowledge of the exam domains. In addition, they also cover exam details and policies. Therefore learning with Online Tutorials will result in strengthening the preparation.
- Online Course- Exam Readiness: AWS Certified Data Analytics – Specialty Online courses are one of the most interactive paths of qualifying for the exam. Subject matter experts make them. Further, the course will give the candidate a solid foundation of the exam domians and concepts. In addition, this online course guides the candidate along the learning curve.
- Try Practice Test– Practice test are the one who ensures the candidate about their preparation. The practice test will help the candidates to acknowledge their weak areas so that they can work on them. There are many practice tests available on the internet nowadays, so the candidate can choose which they want. We at Testprep training also offer practice tests which are very helpful for the ones who are preparing.
We genuinely hope that this article regarding the AWS Certified Data Analytics Specialty Exam helped you In your preparation. One should focus on the course outline and read all the information. Try an online course and free practice tests Now!
