[100% Off] Aws Data Analytics Specialty Certification – Exam Prep Tests
Master AWS Data Analytics Specialty (DAS-C01) with real exam-style practice tests, detailed explanations & 2025 updates
What you’ll learn
- Master AWS Data Analytics Specialty (DAS-C01) exam domains with real-world style practice questions
- Gain hands-on knowledge of AWS data lakes
- Kinesis
- Redshift
- Glue
- and analytics services.
- Understand key concepts of data collection
- storage
- processing
- and visualization on AWS.
- Build confidence with timed practice exams and detailed explanations to pass the certification
Requirements
- Basic understanding of AWS cloud fundamentals is recommended.
- Familiarity with data concepts such as databases
- ETL
- and analytics is helpful.
- AWS Cloud Practitioner or Associate-level knowledge is beneficial but not mandatory
- No advanced coding skills required — everything will be explained through practice tests.
Description
Are you preparing for the AWS Certified Data Analytics – Specialty (DAS-C01) exam? This course is designed to help you pass with confidence through realistic practice tests, updated for 2025.The AWS Data Analytics certification validates your expertise in designing, building, and securing data analytics solutions on AWS. In this course, you’ll get access to comprehensive practice exams that cover all key domains of the certification, including:
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Data Collection Systems – Learn how to use Kinesis, DynamoDB, and streaming services effectively.
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Storage & Data Management – Understand Redshift, S3 data lakes, Glue cataloging, and Athena queries.
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Processing & Transformation – Gain insights into EMR, Glue ETL jobs, and serverless data pipelines.
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Data Analysis & Visualization – Explore AWS QuickSight and BI best practices.
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Security & Monitoring – Strengthen your knowledge on encryption, IAM, and monitoring with CloudWatch.
By the end of this course, you’ll have the knowledge and confidence to ace the AWS Data Analytics Specialty (DAS-C01) certification and advance your career in cloud data engineering, big data, and analytics.