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The AWS Certified Big Data Specialty exam is one of the toughest exams and it requires you a lot of hands-on experience. It is advisable to have prior knowledge of Big Data technologies on AWS. The more experience you have, the more it is beneficial for you, Major challenging things which you can find in exam is about different scenario based questions, you must have strong understanding of Big Data.
AWS Certified Big Data - Specialty Exam validates technical expertise in developing and maintaining Big Data based applications
AWS Certified Big Data - Specialty Exam been designed in order to test expertise and technical knowledge in designing and implementing AWS services in order to obtain value from the available data set. It examines the candidate's ability to execute AWS Big Data Services according to architecture practices. The AWS Certified Big Data-Specialty is a Specialty level exam and it requires in-depth knowledge of data analytics concepts, before you go ahead and write this certification you must require some prerequisites i.e. AWS Certified Cloud Practitioner or an Associate-level AWS Certification and Two or more years of hands-on experience performing complex big data analyses on AWS
AWS has introduced a number of certifications ranging from Associate level to Specialty level. Our AWS Certified Big Data - Specialty Dumps Exam Test Paper been well formatted covering all the following contents in depth:
- Data Pipelines and Security Services
- Design and implementation of Amazon S3
- IOT Rules and Machine Learning
- Design and Implementation of Amazon DynamoDB
- Data Analysis using Redshift
- Business Analytics Service - Quicksight
- Implementation of Kinesis Firehose and Kinesis Stream
Reference: https://aws.amazon.com/certification/certified-big-data-specialty/
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Amazon AWS-Certified-Big-Data-Specialty Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Domain 4: Data Analysis | 17% | - Determine tools and techniques for analysis - Optimize query performance and analysis workflows - Design and architect analytical solutions - Implement analytical operations and transformations |
| Topic 2: Domain 5: Data Visualization | 12% | - Design and build visualization platforms and dashboards - Optimize operational characteristics of visualization systems - Determine appropriate visualization techniques and tools - Ensure accessibility and clarity of presented results |
| Topic 3: Domain 1: Data Collection | 17% | - Identify properties including order, structure, and metadata - Describe availability and durability of collection methods - Select appropriate collection systems based on data type and frequency - Determine operational characteristics of collection systems |
| Topic 4: Domain 6: Data Security | 20% | - Ensure data integrity and compliance - Implement data governance and access control - Apply security best practices for big data workloads - Determine encryption requirements and methods |
| Topic 5: Domain 2: Data Storage | 17% | - Choose appropriate data structures and storage formats - Identify data access and retrieval patterns - Evaluate mechanisms for cataloging and metadata management - Determine and optimize operational characteristics of storage solutions |
| Topic 6: Domain 3: Data Processing | 17% | - Design and architect data processing solutions - Optimize processing performance and cost efficiency - Select appropriate processing frameworks and services - Determine operational characteristics of processing systems |
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