The AWS Certified Data Analytics Specialty Exam is designed to validate a candidate’s skills and expertise in using AWS services for designing and implementing big data solutions. The exam is considered to be challenging and requires a solid understanding of the AWS ecosystem, big data technologies, and data analytics concepts.
The exam consists of 65 multiple-choice and multiple-response questions that need to be answered within 3 hours. The questions are designed to test a candidate’s knowledge of topics such as data collection, processing, storage, and analysis using various AWS services, including Amazon S3, Amazon EMR, Amazon Redshift, Amazon Kinesis, and Amazon QuickSight.
To pass the exam, candidates need to achieve a minimum score of 750 out of 1000. However, the difficulty level of the exam may vary from person to person, depending on their experience and familiarity with the AWS services and data analytics concepts.
Overall, the AWS Certified Data Analytics Specialty Exam is considered to be a challenging exam, but with adequate preparation and hands-on experience with AWS services, candidates can increase their chances of passing the exam.
In this post, we will discover how hard is the AWS Certified Data Analytics Specialty Exam and how to crack it!
About AWS Certified Data Analytics Specialty
The AWS Certified Data Analytics Specialty Exam certificate helps businesses identify and advance personnel with key abilities for carrying out cloud activities. Having an AWS Certified Data Analytics – Specialty certifies that you are knowledgeable about using AWS data lakes and analytics services to extract wisdom from data. The exam also assesses candidates’ knowledge and comprehension of how to design, construct, implement, and defend data analytics solutions on the AWS platform. Additionally, it looks at how the prospect feels about other AWS data analytics services.
Recommended Knowledge
The AWS Certified Data Analytics – Specialty program is intended for users with experience building, designing, securing, and managing analytics applications using AWS services. We advise that you have the following before taking this test:
- First and foremost, five or more years of experience with data analytics tools
- Additionally, the candidate must have at least two years of actual, hands-on experience using AWS.
- Lastly, experience developing, designing, securing, and maintaining analytics systems using AWS services.
Let us now move to the main point –
AWS Certified Data Analytics Specialty Exam: Difficulty Level
An in-depth understanding of data analytics technologies and solutions is required to pass the difficult and complex AWS Data Analytics certification test. In addition to having a thorough understanding of data analysis techniques and methodologies, an analyst with AWS Data Analytics also knows which AWS tool or service should be applied to each issue. There won’t be many questions on fundamental data analytics knowledge on the AWS Data Analytics test, but it will expand on that knowledge. Therefore, you should be familiar with the fundamentals before taking the exam.
You should be familiar with the handling of organized, semi-structured, and unstructured data types, as well as their similarities and differences. Different types of data storage, like data lakes, warehouses, and S3 buckets, should already be familiar to you, along with how to work with them. You should be familiar with OLTP and OLAP systems, as well as batch and stream processing. You ought to be familiar with ACID and BASE compliance and what an ETL process can do to guarantee compliance. Additionally, you must be familiar with the AWS services and pipelines that work with those principles.
Let us now look at the exam format and some resources that can help you ace the exam and ultimately grab a job!
Exam Format
Understanding the test format will help applicants tailor their preparations to the test and its objectives. The applicant will initially need to work toward 65 Multiple Choice and Multi-Response Questions. The candidate will also have 180 minutes to finish it. It is also accessible in English, Korean, Japanese, and Simplified Chinese and will cost them about $300 USD. Most importantly, they must get passing scores between 75% and 80% to earn this certification. Let’s now examine the exam’s course outline.
Course Structure
The applicant should be knowledgeable about these fields and the issues they address.
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)
Now, let’s have a look at some resources!
AWS Exam Guide
Exam Guide for the Exploring AWS learning route is designed for people who work in data analytics roles. Primarily for individuals who wish to execute an AWS Certified Data Analytics Specialty position. Additionally, this can be used as a resource for anyone with beginner-level experience who wants to learn how to design, create, protect, and manage analytics solutions that give insight from data.
Testpreptraining Online Tutorials
The AWS Certified Data Analytics Specialty (DAS-C01) Online Tutorial from Testprep enriches knowledge and offers an in-depth understanding of the test domains. They also go through exam specifics and policies. As a result, learning through online tutorials will improve preparation.
Online Course: AWS Certified Data Analytics
Online Course: AWS Certified Data Analytics, One of the most interactive ways to become exam-qualified, is through online classes. Experts in the field create them. The training will also give the individual a strong foundation in exam topics and ideas. Additionally, the candidate is guided through the learning curve in this online course.
Practice tests
The practice exam will assist the applicants in identifying their areas of weakness so they can improve them. These days, the candidate can select from a variety of practice exams that are available online. Additionally, we at Testprep Training provide practice exams that are highly beneficial for the preparation.
Will the exam be worth investing time and effort in?
AWS Data Analytics is worthwhile for the majority of data-focused IT workers. It’s not worth the time and effort to invest if you don’t work with data models or have to create or implement AWS services around big data. But it’s a valuable credential for advanced professionals who need to know more about organizing data analysis solutions and the numerous data analytic processes involved.
AWS Data Analytics is a fantastic resource for developing your career and establishing your abilities. Consider using AWS Data Analytics if you have experience in data analysis and want a way to document everything you know or show your company how valuable you are. Although it’s a difficult certification to obtain, doing so shows that you have a thorough understanding of AWS tools and services and the range of data analysis.
