AWS Certified Data Engineer Exam Questions
The AWS Certified Data Engineer – Associate exam is designed for individuals who want to validate their skills and knowledge in the field of data engineering, specifically using Amazon Web Services (AWS) technologies. This certification exam tests your ability to design, build, and maintain data systems that allow organizations to process and manage data on AWS at scale. It is ideal for professionals working with large-scale data storage and analytics, as well as those involved in the construction of data lakes, pipelines, and real-time data solutions.
The exam assesses your understanding of key AWS services related to data engineering, such as Amazon S3, Amazon Redshift, AWS Glue, Amazon RDS, and Amazon Kinesis. With data being a critical asset for businesses, the demand for certified AWS Data Engineers continues to rise, making this certification a valuable credential for career growth in the data engineering field.
What You Will Learn from the Exam
The AWS Certified Data Engineer – Associate exam is comprehensive and covers a wide range of topics necessary for data engineering in the cloud. By preparing for and passing the exam, you will gain expertise in the following areas:
Data Collection and Storage:
You will learn to collect and store data efficiently using AWS services like Amazon S3, Amazon DynamoDB, and Amazon RDS.
Understanding of best practices for data storage, including data consistency, security, and scalability.
Data Transformation and Processing:
Gain the skills to use AWS tools such as AWS Glue, Amazon EMR, and Amazon Kinesis to process and transform raw data into actionable insights.
Learn how to create data pipelines and apply data cleaning, filtering, and enrichment techniques.
Data Analytics:
Learn how to leverage AWS services like Amazon Redshift and Amazon Athena for large-scale data analytics.
Understand how to run SQL queries on datasets, perform complex data analysis, and generate reports from structured and unstructured data sources.
Data Security and Compliance:
You will gain a deeper understanding of data governance, security measures, and compliance standards in AWS.
Learn how to protect sensitive data using encryption, access control, and audit logging with services like AWS IAM and AWS Key Management Service (KMS).
Real-Time Data Processing:
Understand how to build and manage real-time data streaming pipelines using Amazon Kinesis and AWS Lambda.
Learn to process continuous data streams for immediate insights and take advantage of real-time analytics.
Who Should Take This Exam?
The AWS Certified Data Engineer – Associate exam is designed for individuals who are actively working in data engineering or related fields. The exam is suitable for professionals who are responsible for managing, processing, and analyzing data using AWS tools and technologies. It is ideal for:
Data Engineers: If you’re working as a data engineer and want to validate your skills in designing and managing large-scale data systems using AWS, this certification is a must.
Data Architects: Professionals who design and manage data architecture in the cloud can benefit from this exam to demonstrate their expertise in AWS-based data solutions.
Business Intelligence Professionals: Those working with analytics and business intelligence platforms using AWS services can further their careers with this certification.
Cloud Practitioners: Individuals who are transitioning into cloud-based data roles or who are looking to specialize in data engineering using AWS technologies will find this exam valuable.
Whether you’re looking to advance your career or shift to a specialized role in data engineering, the AWS Certified Data Engineer – Associate certification will provide you with the skills and recognition you need to succeed in today’s data-driven world.
How to Prepare for the Exam?
Familiarize Yourself with AWS Services:
Start by understanding key AWS services like Amazon S3, AWS Lambda, Amazon Redshift, Amazon Kinesis, AWS Glue, and Amazon RDS.
Review the AWS documentation and whitepapers to deepen your knowledge of how these services are used for data engineering tasks.
Take AWS Training Courses:
AWS offers specialized training courses, including hands-on labs and workshops, to help you prepare for the exam.
You can also consider enrolling in third-party training providers that offer practice exams and study guides tailored to the certification.
Practice with Real-World Projects:
Engage with real-world data engineering scenarios and work with AWS services to build data pipelines, store data, and perform data analytics tasks.
Hands-on practice is crucial for understanding the practical applications of AWS services.
Use Exam Preparation Tools:
Take practice exams to test your knowledge and familiarize yourself with the question format.
Leverage online forums and study groups to interact with other aspirants and share knowledge.
The AWS Certified Data Engineer – Associate certification is an excellent way to demonstrate your skills in building scalable, secure, and efficient data systems on AWS. Whether you’re a seasoned data professional or someone looking to break into the field of data engineering, this certification will equip you with the expertise needed to handle complex data solutions on the cloud. With a growing demand for AWS-certified data professionals, this exam is an essential step towards enhancing your career prospects in the data engineering field.
Sample Questions and Answers
Which AWS service is best suited for real-time data processing and analytics?
A) Amazon Redshift
B) AWS Lambda
C) Amazon Kinesis
D) Amazon RDS
Answer: C) Amazon Kinesis
Explanation:
Amazon Kinesis is designed for real-time data streaming and analytics, enabling you to process large streams of data quickly. It allows for processing in real-time, making it suitable for use cases such as video, logs, or application data.
Which of the following is an example of AWS’s managed NoSQL database service?
A) Amazon RDS
B) Amazon DynamoDB
C) Amazon S3
D) Amazon Aurora
Answer: B) Amazon DynamoDB
Explanation:
Amazon DynamoDB is a fully managed NoSQL database service, designed to handle large-scale, high-performance workloads, particularly for applications that require consistent, low-latency data access.
Which AWS service should be used to store large volumes of unstructured data?
A) Amazon Aurora
B) Amazon S3
C) Amazon EBS
D) Amazon RDS
Answer: B) Amazon S3
Explanation:
Amazon S3 (Simple Storage Service) is ideal for storing unstructured data such as images, videos, and log files. It is highly scalable, durable, and cost-effective for data storage.
What is the maximum size of a single object in Amazon S3?
A) 5 TB
B) 100 GB
C) 1 TB
D) 50 GB
Answer: A) 5 TB
Explanation:
Amazon S3 supports objects up to 5 terabytes in size, which allows for storing extremely large files such as high-definition videos or backup data.
Which of the following AWS services provides fully managed, scalable data warehousing solutions?
A) Amazon RDS
B) Amazon Aurora
C) Amazon Redshift
D) AWS Glue
Answer: C) Amazon Redshift
Explanation:
Amazon Redshift is a managed, petabyte-scale data warehouse service that enables fast querying and analysis of large datasets. It is highly optimized for OLAP (Online Analytical Processing) workloads.
Which AWS service allows you to automate the extraction, transformation, and loading (ETL) process?
A) Amazon Redshift
B) AWS Glue
C) AWS Lambda
D) Amazon Kinesis
Answer: B) AWS Glue
Explanation:
AWS Glue is a fully managed ETL service that allows you to automate the extraction, transformation, and loading of data into a data warehouse or other data stores. It helps to streamline data integration workflows.
Which AWS service is used for running containerized applications?
A) AWS Elastic Beanstalk
B) Amazon ECS
C) AWS Lambda
D) AWS Fargate
Answer: B) Amazon ECS
Explanation:
Amazon ECS (Elastic Container Service) is a highly scalable service for running containerized applications using Docker. It simplifies the deployment and management of containers.
Which type of Amazon RDS instance type is best for read-heavy applications?
A) db.m5.large
B) db.r5.large
C) db.t3.medium
D) db.m5.xlarge
Answer: B) db.r5.large
Explanation:
The db.r5 instance type is optimized for memory-intensive workloads, including read-heavy database applications. It provides better memory and performance than other instance types for such use cases.
What is Amazon S3’s versioning feature used for?
A) To manage object lifecycle policies
B) To track object modifications and changes
C) To create backups of objects
D) To delete objects automatically
Answer: B) To track object modifications and changes
Explanation:
Amazon S3 versioning enables you to keep multiple versions of an object, allowing you to track and retrieve previous versions of data. This is useful for backup and recovery purposes.
What does AWS Data Pipeline provide?
A) Real-time data streaming
B) Data archiving solutions
C) Orchestration of data movement between AWS services
D) Data replication across multiple regions
Answer: C) Orchestration of data movement between AWS services
Explanation:
AWS Data Pipeline is a service that helps automate the movement and transformation of data between different AWS services and on-premises storage. It is particularly useful for scheduled data workflows.
Which AWS service can be used to analyze logs from EC2 instances and other AWS services?
A) AWS Lambda
B) Amazon Elasticsearch Service
C) AWS CloudTrail
D) Amazon QuickSight
Answer: B) Amazon Elasticsearch Service
Explanation:
Amazon Elasticsearch Service is designed for searching, analyzing, and visualizing log data in real-time. It is commonly used for logs analysis, metrics, and troubleshooting.
Which of the following is a feature of Amazon Aurora?
A) Supports both relational and NoSQL data models
B) Fully managed, MySQL and PostgreSQL compatible
C) Only supports PostgreSQL
D) Requires manual backups
Answer: B) Fully managed, MySQL and PostgreSQL compatible
Explanation:
Amazon Aurora is a fully managed relational database service that is compatible with both MySQL and PostgreSQL. It offers high performance and scalability at a fraction of the cost of traditional databases.
What is the purpose of AWS CloudFormation?
A) To monitor AWS resources
B) To automate the provisioning of AWS infrastructure
C) To store infrastructure logs
D) To manage access to AWS services
Answer: B) To automate the provisioning of AWS infrastructure
Explanation:
AWS CloudFormation is a service that allows you to define and provision AWS infrastructure using code. It automates the process of deploying and managing resources like EC2, RDS, and S3.
Which service can be used for cost-effective storage of infrequently accessed data?
A) Amazon S3 Standard
B) Amazon S3 Intelligent-Tiering
C) Amazon S3 Glacier
D) Amazon EBS
Answer: C) Amazon S3 Glacier
Explanation:
Amazon S3 Glacier is a low-cost, long-term storage solution for infrequently accessed data. It is perfect for archiving and compliance use cases where data retrieval times of several hours are acceptable.
Which AWS service is used for creating a data lake architecture?
A) Amazon Redshift
B) AWS Lake Formation
C) Amazon RDS
D) AWS Glue
Answer: B) AWS Lake Formation
Explanation:
AWS Lake Formation is a service that simplifies the process of creating, securing, and managing a data lake in AWS. It enables you to ingest, organize, and analyze data from various sources.
Which service would be used to ensure high availability and data redundancy for Amazon RDS instances?
A) Multi-AZ deployments
B) Amazon S3
C) AWS Lambda
D) Elastic Load Balancer
Answer: A) Multi-AZ deployments
Explanation:
Multi-AZ deployments in Amazon RDS automatically replicate database instances across multiple availability zones, ensuring high availability and disaster recovery.
Which AWS service is designed for processing large datasets in parallel across many instances?
A) AWS Lambda
B) Amazon EC2
C) Amazon EMR
D) AWS Data Pipeline
Answer: C) Amazon EMR
Explanation:
Amazon EMR (Elastic MapReduce) is a cloud-native service designed to process large datasets using distributed computing frameworks like Apache Hadoop, Apache Spark, and others.
What does Amazon Kinesis Data Firehose do?
A) Real-time video processing
B) Real-time stream processing and analytics
C) Streams data to AWS storage and analytics services
D) Provides log monitoring and analysis
Answer: C) Streams data to AWS storage and analytics services
Explanation:
Amazon Kinesis Data Firehose is used to stream data to AWS services like Amazon S3, Amazon Redshift, or Amazon Elasticsearch. It provides a simple and scalable way to deliver data to other AWS services for analytics.
Which service enables automated machine learning (ML) model training and deployment?
A) Amazon SageMaker
B) AWS Lambda
C) AWS Glue
D) Amazon Lex
Answer: A) Amazon SageMaker
Explanation:
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly build, train, and deploy machine learning models at scale.
Which AWS service is best for serving real-time business intelligence dashboards?
A) Amazon QuickSight
B) AWS Lambda
C) Amazon Redshift
D) AWS Glue
Answer: A) Amazon QuickSight
Explanation:
Amazon QuickSight is a fast, cloud-powered business intelligence service that enables users to build and publish interactive dashboards and visualizations based on their data.
Which of the following is used to automatically distribute traffic across multiple targets, such as EC2 instances?
A) Elastic Load Balancer
B) Amazon EC2 Auto Scaling
C) Amazon Route 53
D) Amazon S3
Answer: A) Elastic Load Balancer
Explanation:
Elastic Load Balancer (ELB) distributes incoming traffic across multiple targets, such as EC2 instances, to ensure high availability and fault tolerance for applications.
Which feature of Amazon Redshift helps to speed up query processing?
A) Data partitioning
B) Columnar storage format
C) Real-time streaming
D) AWS Lambda functions
Answer: B) Columnar storage format
Explanation:
Amazon Redshift uses a columnar storage format, which allows for highly efficient queries on large datasets, especially when only a subset of columns is needed for analysis.
What is the primary use case of Amazon RDS Read Replicas?
A) To back up data automatically
B) To increase data availability and redundancy
C) To reduce database latency for read-heavy applications
D) To encrypt data at rest
Answer: C) To reduce database latency for read-heavy applications
Explanation:
Amazon RDS Read Replicas are used to offload read-heavy database workloads from the primary database instance, improving performance and scalability for read operations.
Which AWS service would be most appropriate for storing and querying large amounts of log data?
A) Amazon S3
B) Amazon CloudWatch Logs
C) Amazon RDS
D) AWS Glue
Answer: B) Amazon CloudWatch Logs
Explanation:
Amazon CloudWatch Logs is designed for storing and monitoring log data from applications, EC2 instances, and other AWS services. It allows for real-time log processing and analytics.
Which service allows you to manage data integration workflows across AWS and on-premises resources?
A) AWS Data Pipeline
B) Amazon EMR
C) AWS Glue
D) Amazon Redshift
Answer: A) AWS Data Pipeline
Explanation:
AWS Data Pipeline is a data integration service that allows you to move and process data across AWS services and on-premises resources, providing an easy-to-use solution for managing complex data workflows.
What is the main benefit of Amazon S3’s lifecycle policies?
A) Automatic data encryption
B) Automation of data retention and transition
C) Cost-free storage of data
D) Real-time monitoring of data
Answer: B) Automation of data retention and transition
Explanation:
S3 lifecycle policies allow you to automate the process of transitioning data between different storage classes and deleting old objects, helping to manage data retention and storage costs efficiently.
Which of the following AWS services is used for building, training, and deploying machine learning models on AWS?
A) AWS Glue
B) Amazon SageMaker
C) AWS Lambda
D) Amazon Redshift
Answer: B) Amazon SageMaker
Explanation:
Amazon SageMaker is a comprehensive machine learning service that helps developers build, train, and deploy ML models quickly using pre-built algorithms and frameworks.
Which service should you use to set up a managed Apache Kafka cluster?
A) AWS Lambda
B) Amazon MSK
C) Amazon Kinesis
D) AWS Glue
Answer: B) Amazon MSK
Explanation:
Amazon MSK (Managed Streaming for Kafka) provides a fully managed Apache Kafka service that helps you build real-time streaming applications with ease.
What does Amazon Aurora’s Global Databases feature enable?
A) High availability across multiple regions
B) Real-time streaming of data to other regions
C) Backup of data to Amazon S3
D) Automatic scaling of database instances
Answer: A) High availability across multiple regions
Explanation:
Aurora Global Databases allow you to replicate your database across multiple regions for disaster recovery and high availability, minimizing downtime in the event of a regional failure.
Which service is used to orchestrate and automate ETL jobs in the cloud?
A) AWS Lambda
B) AWS Glue
C) Amazon EMR
D) Amazon QuickSight
Answer: B) AWS Glue
Explanation:
AWS Glue is the preferred service for automating ETL (Extract, Transform, Load) jobs, helping to integrate and transform data for analytics in the cloud.
Which of the following services is used to run SQL queries on data in Amazon S3 without needing to load it into a database?
A) Amazon Redshift Spectrum
B) Amazon RDS
C) AWS Lambda
D) Amazon S3 Select
Answer: D) Amazon S3 Select
Explanation:
Amazon S3 Select allows you to run SQL queries directly on the data stored in Amazon S3 without the need to load it into a separate database, improving performance and reducing costs.
Which AWS service provides data migration capabilities from on-premises to AWS Cloud?
A) AWS DataSync
B) AWS Migration Hub
C) AWS Snowball
D) AWS DMS
Answer: D) AWS DMS
Explanation:
AWS Database Migration Service (DMS) is used to migrate databases from on-premises to AWS or between AWS databases. It supports both homogenous and heterogeneous migrations.
Which of the following is a best practice when working with AWS Lambda for big data workloads?
A) Use a high memory configuration to handle large datasets.
B) Always use a single function to handle all stages of processing.
C) Avoid using multiple Lambda functions to split processing tasks.
D) Use S3 as the sole data source for Lambda.
Answer: A) Use a high memory configuration to handle large datasets.
Explanation:
For big data workloads, AWS Lambda can be configured with more memory, allowing for faster processing of large datasets. Memory size affects CPU power and is crucial for performance.
What is Amazon EMR primarily used for?
A) Relational database management
B) Data warehousing
C) Distributed data processing with Hadoop and Spark
D) Data migration
Answer: C) Distributed data processing with Hadoop and Spark
Explanation:
Amazon EMR (Elastic MapReduce) is used for big data processing tasks using distributed computing frameworks such as Apache Hadoop and Apache Spark.
Which of the following services helps automate the creation of data pipelines and data integration workflows in AWS?
A) AWS Glue
B) Amazon Redshift
C) AWS Lambda
D) Amazon S3
Answer: A) AWS Glue
Explanation:
AWS Glue is a fully managed service designed for automating the creation of data pipelines and data integration workflows, allowing for seamless ETL processes.
Which AWS service should you use to perform SQL-based analytics on semi-structured data?
A) Amazon Aurora
B) Amazon Redshift
C) Amazon Athena
D) AWS Glue
Answer: C) Amazon Athena
Explanation:
Amazon Athena allows for querying semi-structured data (like JSON, Parquet, and ORC) in Amazon S3 using SQL queries. It is serverless, so there is no need to manage infrastructure.
What does Amazon RDS provide for automated backup and recovery?
A) Multi-AZ replication
B) Automated snapshots
C) Elastic Load Balancing
D) Data migration
Answer: B) Automated snapshots
Explanation:
Amazon RDS allows you to automate backups by taking daily snapshots of your database, which can be used for point-in-time recovery. These snapshots can also be manually initiated.
Which AWS service is used to create a scalable serverless architecture for streaming data in real-time?
A) Amazon S3
B) Amazon Kinesis
C) Amazon RDS
D) AWS Glue
Answer: B) Amazon Kinesis
Explanation:
Amazon Kinesis is a real-time data streaming service that enables scalable and serverless data processing. It can capture, store, and analyze streaming data for real-time analytics.
Which of the following can be used to schedule and automate tasks like backups, data migration, and data transformation in AWS?
A) AWS CloudFormation
B) AWS Lambda
C) AWS Data Pipeline
D) Amazon S3 Lifecycle Policies
Answer: C) AWS Data Pipeline
Explanation:
AWS Data Pipeline is a service that enables you to automate workflows, including backup and data transformation tasks, by scheduling recurring operations.
Which of the following is used to process data across multiple sources and formats in AWS?
A) AWS Glue
B) Amazon RDS
C) Amazon S3 Select
D) Amazon EC2
Answer: A) AWS Glue
Explanation:
AWS Glue is an ETL (extract, transform, load) service that processes data across various sources and formats, helping to transform and load data into data lakes or warehouses for analysis.
Which AWS service can be used to run complex queries over large-scale datasets in Amazon S3 with minimal setup?
A) Amazon Redshift
B) AWS Glue
C) Amazon Athena
D) Amazon EMR
Answer: C) Amazon Athena
Explanation:
Amazon Athena is a serverless service that allows you to run SQL queries on large datasets stored in Amazon S3 without the need to set up infrastructure or data warehousing.
Which AWS service can be used for real-time data ingestion and analytics?
A) Amazon Kinesis Data Firehose
B) Amazon S3
C) AWS Lambda
D) Amazon Redshift
Answer: A) Amazon Kinesis Data Firehose
Explanation:
Amazon Kinesis Data Firehose is designed for real-time data ingestion, enabling the streaming of data to destinations like Amazon S3, Amazon Redshift, and Amazon Elasticsearch for analytics.
What is the maximum retention period for data in Amazon Kinesis Data Streams?
A) 1 day
B) 7 days
C) 30 days
D) 365 days
Answer: B) 7 days
Explanation:
Data in Amazon Kinesis Data Streams can be retained for a maximum of 7 days. This period can be extended with an additional configuration but is generally meant for short-term processing.
Which AWS service provides a fully managed graph database service?
A) Amazon Neptune
B) Amazon RDS
C) Amazon DynamoDB
D) Amazon Redshift
Answer: A) Amazon Neptune
Explanation:
Amazon Neptune is a fully managed graph database service that supports graph models like property graphs and RDF graphs. It is optimized for storing and querying relationships between data points.
Which AWS service can be used to set up cross-region replication for an S3 bucket?
A) Amazon CloudFront
B) Amazon S3 Cross-Region Replication
C) AWS Lambda
D) Amazon EC2
Answer: B) Amazon S3 Cross-Region Replication
Explanation:
Amazon S3 Cross-Region Replication (CRR) enables automatic, asynchronous copying of objects across AWS regions, improving data availability and disaster recovery capabilities.
Which AWS service provides continuous data replication across regions for Amazon Aurora?
A) Aurora Global Databases
B) AWS DataSync
C) Amazon S3 Versioning
D) Amazon RDS Multi-AZ
Answer: A) Aurora Global Databases
Explanation:
Aurora Global Databases provide continuous replication of data across multiple regions for Amazon Aurora. This allows for low-latency read operations and fast failover in case of regional failure.
What is the purpose of AWS CloudTrail?
A) To monitor application logs
B) To track user activity and API calls
C) To perform data replication
D) To manage permissions and security
Answer: B) To track user activity and API calls
Explanation:
AWS CloudTrail enables you to track user activity and API calls made on your AWS account, providing visibility into actions taken by users and resources in the AWS environment.
Which AWS service can help you implement an enterprise-level data governance framework?
A) AWS Glue
B) AWS Lake Formation
C) AWS Lambda
D) Amazon RDS
Answer: B) AWS Lake Formation
Explanation:
AWS Lake Formation helps you to create, secure, and manage a data lake. It provides built-in capabilities for data governance, access control, and auditing within a centralized platform.
What is Amazon DynamoDB Accelerator (DAX)?
A) A fully managed caching service for DynamoDB
B) A distributed SQL database
C) A real-time data processing framework
D) A managed backup service for DynamoDB
Answer: A) A fully managed caching service for DynamoDB
Explanation:
Amazon DynamoDB Accelerator (DAX) is a fully managed, in-memory caching service designed to improve the performance of DynamoDB by providing faster read performance.
What is the primary benefit of using AWS Snowball for large-scale data transfers?
A) High-security encryption
B) Real-time data transfer
C) Cost-effective storage for archival purposes
D) Low-latency data transfer over the internet
Answer: C) Cost-effective storage for archival purposes
Explanation:
AWS Snowball is a data transfer appliance that helps move large volumes of data to AWS securely and cost-effectively. It is ideal for situations where internet bandwidth is insufficient for fast data transfer.
Which of the following services provides data validation during the ETL process?
A) AWS Glue
B) AWS Data Pipeline
C) Amazon Redshift
D) Amazon S3
Answer: A) AWS Glue
Explanation:
AWS Glue provides ETL (extract, transform, load) functionality with built-in data validation features, allowing you to ensure the quality of data before it is loaded into the destination.
Which AWS service allows you to run code without provisioning or managing servers?
A) AWS Lambda
B) Amazon EC2
C) AWS Fargate
D) Amazon ECS
Answer: A) AWS Lambda
Explanation:
AWS Lambda is a serverless compute service that runs code in response to events and automatically manages the compute resources. It eliminates the need to manage servers.
Which service is best for managing and querying time-series data?
A) Amazon Redshift
B) Amazon Timestream
C) Amazon RDS
D) Amazon DynamoDB
Answer: B) Amazon Timest ream
Explanation:
Amazon Timestream is a fully managed time-series database service designed to store and analyze time-series data. It is optimized for use cases like IoT, operational monitoring, and real-time analytics.
Which AWS service is used for managing user access and permissions to AWS resources?
A) AWS IAM
B) AWS Shield
C) AWS Lambda
D) AWS WAF
Answer: A) AWS IAM
Explanation:
AWS Identity and Access Management (IAM) is used to manage access to AWS resources securely. It allows administrators to create and manage AWS users, groups, and roles and define permissions.
What is the primary function of Amazon CloudWatch Logs?
A) Monitoring infrastructure performance
B) Storing and querying data logs
C) Running real-time SQL queries
D) Managing AWS accounts and billing
Answer: B) Storing and querying data logs
Explanation:
Amazon CloudWatch Logs allows you to collect, monitor, and store log data from various AWS services and applications. You can query, visualize, and analyze the logs for troubleshooting and performance monitoring.
Which AWS service enables you to automate the deployment of infrastructure as code?
A) AWS CloudFormation
B) Amazon EC2
C) AWS Lambda
D) AWS CodeDeploy
Answer: A) AWS CloudFormation
Explanation:
AWS CloudFormation enables you to define and deploy infrastructure as code, providing automated provisioning and management of AWS resources.
Which of the following tools provides a unified view of data across various AWS services?
A) AWS Glue
B) AWS QuickSight
C) Amazon Redshift
D) AWS DataSync
Answer: B) AWS QuickSight
Explanation:
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that enables you to analyze and visualize data across various AWS services like S3, Redshift, and RDS.
Which service is best for storing and analyzing unstructured data at scale in AWS?
A) Amazon S3
B) Amazon RDS
C) Amazon Redshift
D) Amazon DynamoDB
Answer: A) Amazon S3
Explanation:
Amazon S3 is a highly scalable and durable object storage service, ideal for storing unstructured data at scale, including documents, images, logs, and backups.
Which of the following is an automated solution for detecting sensitive data in your AWS environment?
A) AWS Config
B) AWS Security Hub
C) Amazon Macie
D) AWS IAM
Answer: C) Amazon Macie
Explanation:
Amazon Macie is a machine learning-based service that automatically discovers, classifies, and protects sensitive data like Personally Identifiable Information (PII) in your AWS environment.
Which AWS service is used for providing real-time recommendations based on user behavior?
A) Amazon Kinesis
B) Amazon Rekognition
C) Amazon Personalize
D) Amazon Polly
Answer: C) Amazon Personalize
Explanation:
Amazon Personalize is a fully managed service that uses machine learning to create personalized recommendations for users based on their behavior and preferences.
