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AWS BDS-C00 Exam Certification Details:

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:

• IOT Rules and Machine Learning
• Design and implementation of Amazon S3
• Business Analytics Service - Quicksight
• Data Pipelines and Security Services
• Implementation of Kinesis Firehose and Kinesis Stream
• Design and Implementation of Amazon DynamoDB
• Data Analysis using Redshift

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

iPassleader 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.