AWS Certified Data Engineer Associate Exam Notes and Practice Tests – Practice Test #1

A healthcare company needs to store sensitive patient records in Amazon S3 while ensuring encryption and access control compliance with HIPAA regulations. They want to retain full control over encryption keys and enable automatic key rotation. Which AWS KMS feature will best meet these requirements?

A financial institution is designing a highly available Amazon Redshift cluster to ensure automatic node recovery and scalability during peak usage. Which Redshift configuration should they implement?

A data engineer is designing a DynamoDB table for an online marketplace where users search for products based on category and brand. The table uses ProductID as a unique identifier. How should the engineer structure the table to allow efficient querying by category and brand?

A banking application needs to perform real-time fraud detection by analyzing transaction patterns in streaming data. They require a fully managed AWS service that supports SQL-based real-time analytics. Which service should they use?

A data scientist is working in Amazon SageMaker and needs to restrict access to specific notebooks and models based on user roles. How can the company enforce access control?

A data engineering team processes large volumes of log data in Amazon S3 and loads transformed data into Amazon Aurora PostgreSQL. They want an automated ETL process that minimizes performance impact on Aurora. What is the best approach?

A media company is ingesting log data from multiple applications into Amazon Kinesis Data Streams. They want to ensure data replayability and efficient reprocessing in case of failures. What is the most cost-effective approach?

A film production company needs to transfer terabytes of raw footage from a remote location to Amazon S3 with limited bandwidth. They also want local processing before uploading data to reduce file size. Which AWS service is the best fit?

A data engineer is designing a fraud detection system that processes streaming financial transactions. They need a solution that ensures data integrity and re-ingestion of failed records. Which Kinesis feature set best supports this requirement?

A streaming analytics team at a video streaming company uses Amazon Kinesis Data Streams to capture real-time viewing statistics. They want to distribute streaming data evenly across multiple shards for parallel processing. What role does the partition key play in Kinesis Data Streams?

A company is running an Amazon ECS cluster with the Fargate launch type and wants to ensure that only authorized containers can pull images from Amazon ECR. What is the best approach to secure the ECS-ECR interaction?

A data engineering team is managing an AWS Lake Formation setup. They need to share a subset of data with an external AWS account while enforcing fine-grained access control at the row and column level. What is the best approach?

A company is using Amazon QuickSight to provide dashboards for financial analysts. They need to restrict data access so that each analyst can only view financial data for their department. What QuickSight feature should they use?

A security team wants to monitor Amazon S3 bucket policies and detect unauthorized public access changes in real-time. They also want to trigger alerts when misconfigurations occur. Which AWS services should they use?

A company is running an I/O-intensive database on Amazon EC2 backed by Amazon EBS. They need to optimize cost and performance. Which TWO actions should they take? (Choose Two)

A fintech startup is using Amazon Kinesis to process real-time payment transactions and detect fraud. Multiple consumers need to process the same data stream simultaneously with low latency. What should they use?

A company wants to enforce restricted developer access to Amazon EC2 instances during business hours. They also need to track all changes and automate patch management. Which combination of services should they use?

A data engineer is using AWS Glue DataBrew to clean raw sales data stored in Amazon S3. They need to remove missing values, standardize column names, and normalize date formats. What is the best approach?

A media company needs to migrate multiple petabytes of data to Amazon S3 from an on-premises data center with limited internet bandwidth. They are also concerned about secure transport and potential physical damage to storage devices. Which AWS service should they use?

A data analytics team needs to optimize the performance of a frequently executed SQL query in Amazon Redshift. The query aggregates millions of records and takes a long time to execute. What should they do?

A financial services company needs to ensure that customer data stored in Amazon Redshift meets strict data quality requirements before analysis. The company uses AWS Glue for ETL processing and must validate the data by checking for duplicate records, missing values, and format inconsistencies. Which AWS Glue feature should they use?

A company is designing an event-driven architecture to process messages asynchronously across multiple AWS microservices. Some messages must be broadcasted to multiple consumers, while others need to be processed by only one service. Which combination of AWS services should they use?

A company is performing an AWS compliance audit and needs to review historical AWS API activity and track configuration changes to resources. Which AWS services should they use?

A data engineer needs to run SQL queries asynchronously against an Amazon Redshift cluster from an AWS Lambda function without managing persistent connections. What is the best solution?

A business intelligence team wants to allow non-technical users to ask data-related questions in natural language and receive visual responses in Amazon QuickSight. Which feature should they use?

A social media company is using Amazon Neptune for its graph-based recommendation system. It must scale to handle frequent friend requests, likes, and follows, while optimizing read performance for queries like “Find mutual friends”. What is the best approach to scale Neptune?

A company uses AWS Lambda to process e-commerce orders from an Amazon SQS queue. During peak hours, the queue backlog increases because Lambda is not processing messages fast enough. How can the company improve throughput?

A company is building a serverless ETL pipeline to transform data stored in Amazon S3. They want to deploy the solution as infrastructure-as-code while ensuring automatic scaling based on data volume. What is the best combination of AWS services?

A company is using DynamoDB with DAX enabled to improve performance. However, during peak load, they observe throttling errors. What is the most likely cause, and how can it be mitigated?

A company needs to implement an efficient backup strategy for Amazon EC2 EBS volumes while minimizing storage costs. Which TWO approaches should they use? (Choose Two)

A company is building a centralized security logging system using AWS OpenSearch. They need to implement role-based access control (RBAC) to restrict access to logs based on user roles. What is the best security measure to achieve this?

A video streaming platform needs a scalable, cost-efficient ETL solution to process real-time data from users and store it in Amazon S3. The team needs an auto-scaling ETL service that can run Apache Spark jobs with adjustable compute resources. Which service should they choose?

A company uses DynamoDB with provisioned capacity mode to handle predictable workloads. They want to ensure cost efficiency and performance during occasional traffic spikes. Which TWO features should they enable? (Choose Two)

A social media company is storing user activity logs in DynamoDB. These logs are written at a high velocity and need fast retrieval by user ID to track engagement history. What is the best partition key design for optimizing both write throughput and query performance?

A retail company needs to optimize DynamoDB query performance for multiple access patterns: Retrieve products by category Retrieve products by manufacturer Retrieve product details by Product ID What is the best indexing strategy?

Which of the following best describes how DynamoDB scales compared to relational databases like Amazon RDS?

A data engineer is setting up an Amazon Redshift cluster to store sensitive financial transactions. The company requires: •\tEncryption at rest •\tRestricted network access Which TWO actions should be taken? (Choose Two)

A company wants to share real-time data from its Amazon Redshift cluster with another department’s Redshift cluster in a different AWS region. The consumer cluster should manage its own compute resources while the original data remains unchanged. What is the best solution?

A financial institution needs to detect sensitive customer data (e.g., PII) stored in Amazon S3 for compliance with GDPR. Which AWS service should they use?

A financial company wants to automate the deployment of EC2 instances with the correct security settings while maintaining an audit trail for compliance tracking. Which combination of AWS services should they use?

A healthcare organization is streaming real-time patient vitals using Amazon Kinesis Data Streams. Due to HIPAA compliance, the data must be encrypted in transit and at rest while ensuring long-term immutable storage. Which AWS services and configurations will ensure compliance?

A data engineering team queries sensitive financial data in Amazon Athena stored in Amazon S3. To meet PCI-DSS compliance, they must enforce encryption at rest and in transit. Which TWO actions should they take? (Choose TWO)

A data engineer is preparing a large dataset with missing values and inconsistent formatting for an Amazon SageMaker machine learning model. The engineer prefers a visual interface for data cleaning and transformation. Which SageMaker tool should be used?

A company is using AWS Lambda and Amazon S3 for a serverless image processing pipeline. When an image is uploaded, Lambda resizes it into small, medium, and large formats. However, the function is hitting memory limits and timeout errors for large files. How can the company optimize this workflow while maintaining serverless architecture? (Choose THREE)

A financial institution is storing several years of transaction data in Amazon Redshift. Older data is infrequently queried, while recent data is accessed frequently. How can they optimize cost and performance?

A data engineer is designing a real-time fraud detection pipeline using Amazon Kinesis Data Streams. The system must ensure data durability and availability while handling high throughput. Which Kinesis feature guarantees fault tolerance across multiple Availability Zones?

A company is securing access to its Amazon OpenSearch cluster. They want to integrate authentication with an external identity provider. Which authentication method is NOT supported by OpenSearch?

A company needs to stream application logs to Amazon S3 and Amazon Redshift for analysis. The solution should be fully managed and support near real-time data transformation. Which service is the best choice?

A retail company is using Amazon OpenSearch to store customer order data. They want to ensure that data is highly available, even if a node fails, while keeping the search functionality operational. Which configuration is best?

A company is migrating its on-premises data warehouse to Amazon Redshift. They require continuous data replication with near real-time updates in Redshift. Which service should they use?

A company is using Amazon Redshift for data warehousing and Amazon RDS for PostgreSQL for transactional data. They need to query real-time data from both databases without moving data to Redshift. What is the most efficient way to achieve this?

A data engineer needs to automate a daily ETL process that extracts raw sales data from Amazon S3, transforms it into a Parquet format, and loads it into Amazon Redshift. Which TWO AWS Glue features should the engineer use? (Choose TWO)

A media company stores raw log files in Amazon S3 and needs an automated ETL pipeline to process and load data into Amazon Redshift on a daily schedule. They also want to trigger the process automatically when new data arrives. What should they use?

A company is setting up AWS Lake Formation for a secure and well-organized data lake. The data lake will ingest Amazon S3 and on-premises data. How should they ensure data searchability, metadata cataloging, and access control?

A company operates a web application on Amazon EC2 behind an Elastic Load Balancer (ELB). Traffic fluctuates with peak hours, requiring cost-effective yet highly available infrastructure. What should they implement?

A data engineering team needs to process and store clickstream data from an e-commerce website in a cost-efficient manner. The data must be queried in real-time while keeping storage costs low. Which TWO solutions should they implement? (Choose TWO)

A company needs to automate the rotation of database credentials while ensuring security best practices. Which AWS service should they use?

A retail company is using Amazon SageMaker for personalized product recommendations. They need a low-latency feature store for real-time predictions and an offline store for batch processing. Which SageMaker capability should they use?

A healthcare company must monitor Amazon S3 buckets for sensitive health data and detect unauthorized access patterns to prevent data breaches. Which Amazon Macie feature should they use?

A company is designing a real-time ingestion system for streaming analytics. They need a stateful processing solution that tracks processed data to avoid duplication. What AWS service should they use?

A company is storing large amounts of archival data in Amazon S3. To reduce costs, they want to move older data to a more cost-effective storage class while ensuring that data retrieval takes only a few minutes when needed. Which Amazon S3 storage class should they use?

A data engineering team is processing real-time IoT sensor data using AWS Lambda and Amazon Kinesis Data Streams. Each Lambda function processes a batch of records and writes the results to Amazon S3. The company wants to ensure that the Lambda function remains stateless but can still maintain some context across invocations. Which statement best describes this behavior?

A financial company needs to process log data stored in Amazon S3, convert it into columnar format, and load it into Amazon Redshift for efficient querying. The ETL process should run hourly and be fully automated. What is the best AWS Glue approach to meet this requirement?

A data engineering team is processing customer transaction data that contains personally identifiable information (PII) such as credit card numbers and social security numbers. They need to automatically identify and mask PII fields before storing the data in Amazon Redshift. Which AWS Glue transformation should they use?

A company is analyzing log data stored in Amazon S3 using Amazon Athena. The logs are in JSON format, but queries are slow due to the large data size. The company wants to optimize query performance while reducing the amount of data scanned. Which approach should the data engineer take?