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Amazon AWS-Big-Data-Specialty exam : AWS Certified Big Data - Specialty

AWS-Big-Data-Specialty Exam Simulator
  • Exam Code: AWS-Big-Data-Specialty
  • Exam Name: AWS Certified Big Data - Specialty
  • Updated: Jul 18, 2026
  • Q & A: 262 Questions and Answers
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AWS Big Data Specialty Exam Syllabus Topics:

SectionObjectivesWeight
Visualization-Determine the appropriate techniques for delivering the results/output
-Determine how to design and create the Visualization platform
-Determine and optimize the operational characteristics of the Visualization system
12%
Processing-Identify the appropriate data processing technology for a given scenario
-Determine how to design and architect the data processing solution
-Determine the operational characteristics of the solution implemented
17%
Storage-Determine and optimize the operational characteristics of the storage solution
-Determine data access and retrieval patterns
-Evaluate mechanisms for capture, update, and retrieval of catalog entries
-Determine appropriate data structure and storage format
17%
Analysis-Determine the tools and techniques required for analysis
-Determine how to design and architect the analytical solution
-Determine and optimize the operational characteristics of the Analysis
17%
Collection-Determine the operational characteristics of the collection system
-Select a collection system that handles the frequency of data change and type of data being ingested
-Identify the properties that need to be enforced by the collection system: order, data structure, metadata, etc.
-Explain the durability and availability characteristics for the collection approach
17%
Data Security-Determine encryption requirements and/or implementation technologies
-Choose the appropriate technology to enforce data governance
-Identify how to ensure data integrity
-Evaluate regulatory requirements
20%

Reference: https://d1.awsstatic.com/training-and-certification/docs-bigdata-spec/AWS%20Certified%20Big%20Data%20-%20Specialty_Exam%20Guide_v1.4_FINAL.pdf

AWS Certified Big Data - Specialty Exam Dumps Provided Study Notes

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- Kinesis Firehose and Kinesis Stream: Kinesis firehose and Kinesis stream are big data terms associated with streaming real time data to destinations such as S3, ES and spunk. You would require to know the importance of Kinesis and combining it with Lambda. Also learn where the Firehose will unload the content. Also understand why kinesis ingestion is better. Learn features offered by KCL and KPL. Also understand when to use them to send/receive data.

- S3: Amazon S3 is storage for internet: It is a simple web service interface which is used to store and retrieve data anywhere anytime. You would require to know how to use S3 buckets, when to use S3 standards and S3 glacier.

- DynamoDB: DymanoDB is a NoSQL database services for all application that need consistent, single digit millisecond latency at any scale. Study the right use case for DymanoDB. How to design LSI and GSI. You might get asked about key optimization.

- Quicksight: It is a fast cloud-powered business analytics service. You may be asked a question on virtual representation with Quicksight.

- Redshift: AWS redshift is a fast, fully managed data warehouse which helps you analyze your data using SQL and BI. You will need to know how to load data using Redshift. You will need to understand the overall architecture. Leader, node slices, compute node. Redshift is an important topic and you would require to know the importance of the COPY command. Also to migrate data using UNLOAD command. Tuning performance use right sort key. At least one question on loading data in Redshift is asked. For this you should know what slices are and instances.

- IoT: The questions will be asked mixed with topics like as DynamoDB and Kinesis Stream. You will require knowledge of IoT rules, Device Registry, Device Gateway, Device Shadow, etc.

- Machine Learning: Knowledge of which types of models are by AWS Machine learning. Also understand when to select right model for specific business requirements and scenarios.

- Security: IAM roles, data encryption for S3, Redshift and DynamoDB, HSM.

- Data Pipeline: Some questions will be asked on Data pipeline, Data Pipeline and Lambda and when to us it. Questions on backup, restore data into other AWS regions.

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AWS Certified Big Data - Specialty Dumps will include below mentioned topics with Exam focused percentage

AWS Certified Big Data - Specialty Dumps will include below mentioned topics with Exam focused percentage:

  • Data Security 20%
  • Analysis 17%
  • Storage 17%
  • Visualization 12%
  • Processing 17%
  • Collection 17%

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AWS Certified Big Data - Specialty Exam Dumps will be helping you

AWS Certified Big Data - Specialty Exam Dumps will be helping you out to overcome with all these hurdles. For more info visit: - AWS Certified Big Data - Specialty Exam Reference - AWS Whitepapers

  • AWS Database Migration Service Best Practice.
  • Big Data Analytics Options on AWS.
  • Migrating Applications to AWS: Guide and Best Practices. - AWS Documentation
  • Streaming Data Solutions on AWS with Amazon Kinesis.

Amazon AWS-Big-Data-Specialty Exam Syllabus Topics:

SectionWeightObjectives
Data Security20%- Choose governance and access control mechanisms
- Ensure data integrity and compliance
- Determine encryption requirements and technologies
- Evaluate security against regulatory needs
Collection17%- Determine operational characteristics of collection systems
- Explain durability and availability aspects
- Identify data properties and structure requirements
- Select appropriate data ingestion mechanisms
Storage17%- Data access and retrieval patterns evaluation
- Mechanisms for catalog capture/update
- Appropriate storage formats and data structure
- Determine and optimize storage characteristics
Analysis17%- Optimize analytical performance and operations
- Design analytical solutions
- Determine tools and techniques for analysis
Processing17%- Evaluate operational characteristics of solutions
- Select appropriate processing technologies
- Design and architect processing solutions
Visualization12%- Design visualization solutions
- Optimize visualization performance
- Determine visualization methods

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