عراب ال IT 
امتحانات دوليه
(DAS-C01)
AWS Certified Data Analytics – Specialty
About the Certification
This is an advanced professional AWS certification within the Data Specialty track, focused on designing, building, and analyzing cloud-based data solutions using AWS services such as Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, DynamoDB, Athena, Amazon S3, and QuickSight, among others. The certification covers the entire data lifecycle:
Data ingestion → Storage → Processing → Analytics → Governance and Security. It is designed for data analytics specialists and validates your ability to build high-performance, reliable, and scalable data analytics platforms on AWS.
Main Curriculum
The curriculum is divided into six main sections
- Data Ingestion
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- Data ingestion using Kinesis, Firehose, DMS, and AWS IoT Core
- Streaming and batch processing patterns
- Analytics and Visualization
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- Data analysis using Redshift, Athena, EMR, and QuickSight
- BI dashboard design
- Query and performance optimization
- Data Storage
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- Data storage using Amazon S3, Redshift, DynamoDB, and RDS
- Data Lake design
- Best practices
- Partitioning and compression
- Security and Governance
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- Identity and Access Management (IAM)
- Encryption using AWS KMS
- AWS Lake Formation
- Data governance and access control
- Data cataloging
- Data Processing
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- Data processing using Glue, EMR, Lambda, Athena, and Redshift Spectrum
- Building ETL / ELT pipelines
- Apache Spark and Hadoop concepts
- Monitoring and Optimization
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- System monitoring with Amazon CloudWatch
- Storage and processing performance optimization
- Cost optimization in AWS Analytics environments
Exam Details
- Exam code: DAS-C01
- Duration: 180 minutes
- Number of questions: 65
- Question types: Multiple-choice and multiple-response
- Language: English
- Passing score: 750 out of 1000
- Exam fee: USD 300 (may vary by country and may include taxes)
- Certification validity: 3 years
Do I need certifications before it ?
No, not at all.
However, it is recommended to have a solid understanding of core AWS services, hands-on experience in data engineering or ETL processes, and knowledge of SQL and Big Data fundamentals.
Holding the AWS Solutions Architect – Associate certification or having practical experience as a Data Engineer is considered a strong advantage.
Which certifications are recommended after this one ?
Not mandatory. However, for professionals seeking a strong path in data analytics on AWS especially in designing analytics solutions, processing large-scale data, and building decision-support dashboards the AWS Certified Data Analytics – Specialty certification is the strongest specialization.
What are the main career uses of the AWS Certified Data Analytics – Specialty ?
- Advanced Data Engineering (Glue / Redshift / Lakehouse) for enterprise-scale data platform design.
- AWS Machine Learning – Specialty to transition from analytics to data-driven ML solutions.
- Generative AI (Data for LLMs / RAG) to power GenAI applications with analytics-ready data.
- Data Science tracks (Python / Statistics / ML) for advanced analytics and data science roles.
- BI & Visualization (QuickSight / Tableau / Power BI) to transform data into actionable insights.
- Data Governance & Security (IAM / Lake Formation) to specialize in data governance, quality, and compliance.
Your Step Toward Advanced Data Analytics Mastery :
This certification enables you to work with large-scale data workloads on AWS, build advanced analytics systems, and deliver the performance and flexibility required by data-driven organizations. It is a powerful step for any Data Engineer or Cloud Architect aiming to specialize professionally in Cloud Data Engineering.
Who is this certification suitable for ?
- Data Engineers, Data Analysts, and Data Architects.
- Professionals with AWS experience who want to specialize in data and analytics.
- Engineers building data pipelines or ETL/ELT systems on AWS.
- Professionals working with Big Data or Machine Learning who need a strong data processing foundation.
- Anyone targeting roles such as Data Engineer, BI Engineer, Analytics Specialist, or Cloud Data Architect.