AWS Certified Big Data - Specialty

AWS Certified Big Data Specialty

Demonstrate your expertise in designing and operating a scalable, secure, and highly performant big data platform on the AWS cloud.

Focus

The AWS Certified Big Data - Specialty certification focuses on Big Data on AWS

Exam Duration

The AWS Certified Big Data - Specialty exam duration is 170 minutes

Num of Question

The exam contains a total of 95

Exam Fee

The exam fee is 300 USD.

Exam Validity

The CCRN-Pediatric certification is valid for 2 years.

Prior Certification

• None

Exam Format

The AWS Certified Big Data - Specialty exam is a Multiple-choice questions

Passing Conditions

To pass AWS Certified Big Data - Specialty exam, applicants must achieve a minimum score of Passing score is determined by Amazon

Who Should Take

Individuals and organizations that handle, analyze, and interpret large-scale data sets in the cloud.

• Data Architect
• Data Engineer
• Data Scientist

Prerequisites

At least 24 months of hands-on experience designing and implementing big data workloads on AWS.

Or can substitute with certifications:

• None

Certification Process

• Self-study using AWS documentation and official courses (e.g., šW Big Data on AWS)
• Hands-on experience with AWS big data services
• Practice with sample questions and lab environments
• Schedule and take the exam

Domains with Weights

• Domain 1: Designing Amazon Elastic MapReduce (Amazon EMR) and Data Warehousing (DWH) Solutions (22%): Design and implement efficient big data processing pipelines using Amazon EMR, Hive, and Redshift;
• Domain 2: Collecting and Managing Data in a Scalable and Efficient Manner (20%): Determine the most suitable data ingestion and processing methods, including Amazon Simple Storage Service (Amazon S3), Amazon Kinesis, Amazon DynamoDB, and Amazon Redshift;
• Domain 3: Designing and Implementing Real-Time Data Processing Solutions (14%): Design and implement efficient real-time data processing pipelines using Amazon Kinesis and Amazon Redshift Data Warehouse;
• Domain 4: Designing and Implementing Big Data-Driven Applications and Data Science Workflows (22%): Determine the most suitable data science workflow, including machine learning, deep learning, and a data preparation tools like Apache Spark, Apache Hadoop, and Amazon SageMaker;
• Domain 5: Designing and Implementing Big Data Data Governance and Security Solutions (22%): Design and implement effective big data data governance and security solutions using AWS IAM, Cognito, and KMS, including data encryption, access control, and auditing;