AWS Certified Big Data Specialty Exam Questions

The AWS Certified Big Data – Specialty Exam is designed for individuals with a deep understanding of big data technologies and AWS services. This certification validates your expertise in designing and implementing big data solutions using AWS tools, making it ideal for professionals in roles like data engineers, architects, and analysts.

Who Should Take the Exam?

This exam is best suited for professionals who work with large-scale data sets and are proficient in big data solutions. Ideal candidates include data engineers, data architects, data analysts, and professionals involved in data processing, storage, and analysis. A solid foundation in AWS services, data analytics, and hands-on experience with AWS tools is crucial for success.

Key Topics Covered

The AWS Certified Big Data – Specialty Exam covers a broad range of topics crucial for working with big data on the AWS cloud. These topics include:

Data Collection: Learn how to capture real-time data using services like Amazon Kinesis and AWS Glue.
Data Storage and Management: Understand the best practices for storing large datasets using Amazon S3, DynamoDB, and Amazon Redshift.
Data Processing: Master data processing techniques with Amazon EMR, AWS Lambda, and AWS Data Pipeline.
Security and Compliance: Gain insights into securing big data solutions with AWS Identity and Access Management (IAM), encryption, and access controls.
Data Analysis and Visualization: Dive into querying large datasets using Amazon Athena, Amazon QuickSight, and Redshift Spectrum.

Exam Preparation

To succeed in the AWS Certified Big Data – Specialty Exam, it’s recommended to have hands-on experience with AWS services related to big data and analytics. Candidates should also take advantage of AWS training resources and practice exams to get familiar with the format and types of questions.

This certification proves your ability to handle large-scale data processing and analytics on AWS, setting you apart as a skilled professional in the growing big data industry.

AWS Big Data Exam – Sample Questions and Answers

1. A company stores clickstream data in Amazon S3 and wants to run SQL queries on the data with minimal infrastructure management. What is the best solution?

A. Amazon Redshift
B. Amazon EMR
C. Amazon Athena
D. Amazon RDS

Answer: C. Amazon Athena
Explanation: Amazon Athena allows you to run SQL queries directly on data stored in Amazon S3 using a serverless architecture. It’s ideal for ad hoc querying without managing infrastructure.

2. Which AWS service provides automatic scaling of resources based on the volume of incoming streaming data?

A. Amazon EMR
B. Amazon Kinesis Data Analytics
C. Amazon Athena
D. AWS Glue

Answer: B. Amazon Kinesis Data Analytics
Explanation: Kinesis Data Analytics can automatically scale to match the incoming data stream load, providing real-time analytics without manual intervention.

3. You need to catalog and prepare large datasets for analytics. Which AWS service should you use?

A. Amazon RDS
B. AWS Glue
C. Amazon SQS
D. AWS Lambda

Answer: B. AWS Glue
Explanation: AWS Glue is a fully managed ETL service that helps discover, catalog, and transform data for analytics.

4. What AWS service allows for near real-time log analytics from applications?

A. Amazon Redshift
B. Amazon S3
C. Amazon CloudWatch
D. Amazon OpenSearch Service

Answer: D. Amazon OpenSearch Service
Explanation: OpenSearch (formerly Elasticsearch) is ideal for real-time log analytics and full-text search.

5. Which format is best suited for storing large analytical datasets in S3 for use with Athena?

A. CSV
B. JSON
C. XML
D. Parquet

Answer: D. Parquet
Explanation: Apache Parquet is a columnar storage format that is optimized for analytical queries, reducing scan costs and improving performance.

6. Which service allows ingestion and real-time processing of IoT sensor data?

A. Amazon Kinesis Data Streams
B. Amazon S3
C. AWS Batch
D. Amazon EC2

Answer: A. Amazon Kinesis Data Streams
Explanation: Kinesis Data Streams allows you to ingest high-throughput, real-time data such as IoT sensor streams for further processing.

7. A company needs to orchestrate a workflow that runs complex ETL tasks with dependency management. What service should they use?

A. AWS Step Functions
B. AWS Lambda
C. AWS Glue
D. Amazon Kinesis Data Firehose

Answer: A. AWS Step Functions
Explanation: AWS Step Functions coordinate workflows with complex dependencies and retries, ideal for ETL orchestration.

8. You are using Amazon Redshift and want to reduce query times. What feature should you use?

A. Data Pipelines
B. Column-level encryption
C. Redshift Spectrum
D. Sort and distribution keys

Answer: D. Sort and distribution keys
Explanation: Properly configured sort and distribution keys help optimize query performance in Redshift by minimizing data movement.

9. What AWS service provides machine learning capabilities integrated with Spark-based big data workloads?

A. AWS Glue
B. Amazon Redshift ML
C. Amazon SageMaker
D. Amazon EMR

Answer: D. Amazon EMR
Explanation: Amazon EMR supports Apache Spark, allowing the use of ML libraries such as MLlib on big data workloads.

10. Which AWS service allows automatic schema inference during ETL jobs?

A. Amazon Athena
B. Amazon EMR
C. AWS Glue
D. Amazon Redshift

Answer: C. AWS Glue
Explanation: AWS Glue crawlers automatically infer schemas from data in S3 or JDBC sources, simplifying ETL.

11. Which AWS service is best suited for creating a data lake on AWS?

A. Amazon DynamoDB
B. Amazon S3
C. Amazon Aurora
D. Amazon RDS

Answer: B. Amazon S3
Explanation: S3 is highly durable, scalable, and cost-effective for storing vast amounts of structured and unstructured data, making it ideal for data lakes.

12. Which of the following supports streaming ingestion directly into Amazon Redshift?

A. Amazon Kinesis Data Firehose
B. AWS Glue
C. AWS Data Pipeline
D. Amazon EMR

Answer: A. Amazon Kinesis Data Firehose
Explanation: Kinesis Data Firehose can deliver streaming data directly into Redshift, S3, or OpenSearch Service.

13. Which file format supports schema evolution and is ideal for data lakes?

A. CSV
B. JSON
C. Avro
D. XML

Answer: C. Avro
Explanation: Apache Avro supports schema evolution, making it suitable for big data environments and data lakes.

14. Which service integrates easily with AWS Glue Data Catalog for running SQL queries?

A. Amazon RDS
B. Amazon Athena
C. Amazon EC2
D. AWS Lambda

Answer: B. Amazon Athena
Explanation: Athena directly integrates with the Glue Data Catalog for schema and metadata management.

15. A company needs a dashboard that refreshes in near-real-time using data from Redshift. What should they use?

A. Amazon CloudWatch
B. Amazon QuickSight with direct query
C. AWS Lambda
D. Amazon EMR

Answer: B. Amazon QuickSight with direct query
Explanation: Amazon QuickSight can query Redshift directly to provide up-to-date dashboards without data duplication.

16. Which Redshift feature allows querying data in S3 without loading it into Redshift tables?

A. Redshift Enhanced VPC Routing
B. Redshift Spectrum
C. Redshift Concurrency Scaling
D. Redshift AQUA

Answer: B. Redshift Spectrum
Explanation: Redshift Spectrum allows Redshift to query S3-based data directly using external schemas.

17. Which AWS service allows triggering ETL jobs based on S3 events?

A. AWS Lambda
B. AWS Glue
C. Amazon EMR
D. Amazon Athena

Answer: B. AWS Glue
Explanation: AWS Glue can be configured to run ETL jobs in response to S3 event notifications using crawlers and triggers.

18. What tool allows data engineers to author, run, and monitor pipelines visually on AWS?

A. AWS Glue Studio
B. AWS Step Functions
C. AWS CloudTrail
D. Amazon CloudWatch

Answer: A. AWS Glue Studio
Explanation: AWS Glue Studio provides a visual interface for creating and managing ETL pipelines.

19. Which encryption options are available for Amazon S3 data? (Choose two)

A. SSL
B. SSE-S3
C. SSE-KMS
D. VPN

Answer: B. SSE-S3, C. SSE-KMS
Explanation: Amazon S3 supports server-side encryption with S3-managed keys (SSE-S3) and AWS KMS-managed keys (SSE-KMS).

20. What AWS service can help monitor and visualize streaming data metrics in real time?

A. Amazon QuickSight
B. Amazon CloudWatch
C. Amazon Kinesis Data Analytics
D. AWS Glue

Answer: C. Amazon Kinesis Data Analytics
Explanation: Kinesis Data Analytics allows running SQL on streaming data and outputs metrics for real-time dashboards.

21. Which AWS service supports row-level security (RLS) in dashboards?

A. AWS Glue
B. Amazon Athena
C. Amazon QuickSight
D. Amazon Redshift

Answer: C. Amazon QuickSight
Explanation: QuickSight supports row-level security to restrict data access at the user level.

22. What Amazon Redshift feature allows workload isolation across teams?

A. Workload Management (WLM)
B. Sort keys
C. Distribution styles
D. Columnar compression

Answer: A. Workload Management (WLM)
Explanation: Redshift WLM allows configuring queues and memory for concurrent workloads, enabling isolation.

23. Which of the following best helps optimize partitioning for large S3 datasets?

A. Use of JSON format
B. Using KMS encryption
C. Data partitioning by date or region
D. Using CSV format

Answer: C. Data partitioning by date or region
Explanation: Partitioning by frequently filtered fields like date or region helps reduce query scan cost in Athena and Glue.

24. What service is used to convert semi-structured logs into structured datasets?

A. AWS Glue
B. AWS CloudTrail
C. Amazon RDS
D. AWS IAM

Answer: A. AWS Glue
Explanation: Glue can transform JSON, CSV, and other semi-structured data into structured formats for analysis.

25. What Amazon EMR feature allows cost optimization by using spot and on-demand instances together?

A. Instance Groups
B. Cluster Auto Scaling
C. EMR Managed Scaling
D. Instance Fleets

Answer: D. Instance Fleets
Explanation: EMR Instance Fleets let you mix spot and on-demand instances to balance cost and performance.

26. What should you use for a serverless and fully managed data integration solution?

A. Amazon RDS
B. Amazon EC2
C. AWS Glue
D. Amazon EMR

Answer: C. AWS Glue
Explanation: Glue is serverless, managed, and specifically built for ETL and data preparation tasks.

27. A company needs to send data from S3 to Redshift daily. What is the best choice?

A. Amazon Athena
B. Amazon Kinesis
C. AWS Glue
D. AWS Batch

Answer: C. AWS Glue
Explanation: Glue ETL jobs can schedule and automate data movement from S3 to Redshift.

28. Which AWS service allows transforming and filtering streaming data before delivering it?

A. Amazon EMR
B. AWS Lambda
C. Kinesis Data Firehose
D. Amazon SQS

Answer: C. Kinesis Data Firehose
Explanation: Firehose supports basic data transformations using Lambda before delivery to targets like S3, Redshift, or OpenSearch.

29. How can you monitor query execution and performance in Amazon Athena?

A. Amazon QuickSight
B. AWS Glue Catalog
C. Amazon CloudTrail and CloudWatch
D. Amazon Redshift Spectrum

Answer: C. Amazon CloudTrail and CloudWatch
Explanation: Athena logs query execution data to CloudTrail and CloudWatch for auditing and performance monitoring.

30. You are building a data warehouse solution on AWS. Which combination is ideal for high-performance analytics?

A. Amazon RDS and CloudWatch
B. Amazon Redshift and S3
C. Amazon Athena and DynamoDB
D. AWS Glue and Lambda

Answer: B. Amazon Redshift and S3
Explanation: Redshift integrates tightly with S3 (via Redshift Spectrum) for scalable, high-performance analytics across structured and unstructured data.