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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:
- Collection 17%
- Storage 17%
- Data Security 20%
- Processing 17%
- Visualization 12%
- Analysis 17%
Steps for AWS Certified Big Data - Specialty Certifications Exam Booking
- Visit to website https://www.aws.training
- Signup/Login to AWS account
- Search for AWS Certified Big Data - Specialty Certifications Exam
- Select Date and Center of examination and confirm with payment value of 300$
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AWS Certified Big Data - Specialty Exam Dumps Provided Study Notes
LatestCram expert team recommend you to prepare some notes on these topics alongwith it don't forget to practice AWS Certified Big Data - Specialty Exam Dumps which been written by our expert team.
Both these will help you a lot to clear this exam with good grace.
- 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 Big Data Specialty Exam Syllabus Topics:
| Section | Objectives | Weight |
|---|---|---|
| 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% |
| 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% |
| 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% |
| 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% |
| 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% |
| 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% |
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