A company needs to store semi-structured transactional data for an application in a database. The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?
A. Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.
B. Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.
C. Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.
D. Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.
Show Answer
Correct Answer: D
Explanation: Amazon DynamoDB is a fully managed serverless NoSQL database designed for semi-structured data. It provides single-digit millisecond reads, and adding DynamoDB Accelerator (DAX) further reduces read latency for frequently accessed data. The workload is write-infrequent and read-heavy, making DAX an appropriate fit. Amazon S3 and Iceberg are object storage/analytics solutions rather than low-latency transactional databases, and Amazon RDS is not serverless in the configuration described.
Question 18
A company needs a solution that restricts access to Amazon S3 data and encrypts the data by using AWS managed keys. The solution must manage database credentials that an AWS Lambda function uses and must rotate the credentials automatically.
Which solution will meet these requirements?
A. Use S3 bucket policies to control access. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the data. Store the database credentials as Lambda environment variables.
B. Use IAM policies to control access. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the data. Configure AWS Secrets Manager to store and automatically rotate the credentials by using a Lambda function.
C. Use S3 ACLs to control access. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the data. Store the credentials in AWS Systems Manager Parameter Store and automatically rotate the credentials by using a Lambda function.
D. Use IAM policies to control access. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the data. Store the credentials in AWS Systems Manager Parameter Store. Configure a scheduled Lambda function to rotate the credentials.
Show Answer
Correct Answer: B
Explanation: IAM policies are the recommended mechanism to control access to Amazon S3. Encryption with AWS managed keys is satisfied by SSE-KMS using AWS KMS keys managed by AWS. AWS Secrets Manager is designed to securely store database credentials and supports automatic rotation through a Lambda rotation function. SSE-S3 uses S3-managed keys rather than AWS KMS managed keys, and storing credentials in Lambda environment variables or Parameter Store does not best meet the automatic database credential rotation requirement.
Question 19
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks. The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student’s selected language.
Which solution will meet these requirements with the LEAST operational overhead?
A. Build a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queues the database when generating responses with Amazon Bedrock.
B. Create a knowledge base in Amazon Bedrock Knowledge Bases with the university’s textbooks. Configure a multilingual model to generate responses with source citations.
C. Use Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.
D. Use Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university’s textbooks to generate personalized feedback with citations.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock Knowledge Bases provides a managed RAG solution that indexes the university textbooks, retrieves relevant passages, and can return source citations. Using a multilingual foundation model enables responses in the student's selected language while grounding answers in the course materials. This avoids building and operating a custom vector database, search pipeline, or hosting a fine-tuned model, resulting in the least operational overhead.
Sources:
https://aws.amazon.com/blogs/publicsector/empowering-personalized-learning-at-scale-loyola-marymount-universitys-ai-course-companion
Question 20
A media company uploads large video files to Amazon S3 for processing. After processing, the company needs to keep the original files for 90 days in case the files require reprocessing. After 90 days, the company can delete the files to reduce storage costs. The company stores the processed videos in a different S3 bucket.
Which S3 Lifecycle configuration will meet these requirements for the original files MOST cost-effectively?
A. Store the files in S3 Standard for 90 days. Transition the files to S3 Glacier Flexible Retrieval for long-term storage. Then expire the files.
B. Store the files in S3 Standard for 90 days. Enable versioning. Enable Object Lock on the files for 90 days. Then expire the files.
C. Store the files in S3 Standard for 90 days. Implement S3 Lifecycle management to expire the files.
D. Store the files in S3 Intelligent-Tiering for 90 days. Enable versioning. Add S3 Lifecycle management to expire the files.
Show Answer
Correct Answer: C
Explanation: The requirement is to retain the original objects for 90 days and then delete them. There is no need for archival beyond 90 days, versioning, Object Lock, or Intelligent-Tiering. A lifecycle expiration rule that deletes objects after 90 days is the simplest and most cost-effective solution while keeping the objects in S3 Standard during the required retention period.
Question 21
A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow.
Which solutions will meet these requirements? (Choose two.)
A. Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
B. Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
C. Provision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined scheduleю
D. Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
E. Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
Show Answer
Correct Answer: A, B
Explanation: AWS Step Functions is a serverless workflow orchestration service that can coordinate AWS Glue jobs, manage branching, retries, and error handling. AWS Glue Workflows is purpose-built to orchestrate Glue crawlers, jobs, and triggers with dependency graphs. An always-on EC2 instance is not serverless, EventBridge alone is not designed for complex workflow orchestration and dependency management, and CodePipeline is intended for CI/CD rather than ETL workflow orchestration.
Question 22
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.
Which solution will meet these requirements?
A. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
B. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
C. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
D. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Show Answer
Correct Answer: D
Explanation: Athena Federated Query can query and join data across supported external data sources such as Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 without first centralizing or moving the data. For a one-time performance report, this minimizes unnecessary data movement while enabling cross-source analysis.
Question 23
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
B. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.
C. Deploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.
D. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
Show Answer
Correct Answer: B
Explanation: AWS Step Functions Distributed Map is designed to process very large datasets from Amazon S3 in parallel with managed orchestration. The ToleratedFailurePercentage field provides built-in failure threshold handling, allowing the workflow to stop when more than 15% of items fail. This satisfies the parallel processing and error handling requirements with the least operational overhead compared to managing AWS Batch, EMR, or custom tracking with Lambda and S3 Batch Operations.
Question 24
A company needs to collect logs for an Amazon RDS for MySQL database and make the logs available for audits. The logs must track each user that modifies data in the database or makes changes to the database instance.
Which solution will meet these requirements?
A. Enable Amazon CloudWatch Logs. Create metric filters to monitor database changes and instance-level changes. Configure automated notification systems to send near real-time alerts for suspicious database operations.
B. Configure an Amazon EventBridge rule to monitor database activity. Create an AWS Lambda function to process EventBridge events and store them in Amazon OpenSearch Service.
C. Configure AWS CloudTrail to log API calls. Use Amazon CloudWatch Logs for basic monitoring. Use IAM policies to control access to the logs. Set up scheduled reporting for log audits.
D. Enable and configure native Amazon RDS database audit logging. Enable Amazon CloudWatch Logs. Configure metric filters and alarms. Configure AWS CloudTrail audit logging.
Show Answer
Correct Answer: D
Explanation: Tracking users who modify data inside an Amazon RDS for MySQL database requires native database audit logging, because AWS CloudTrail records AWS API (management plane) activity rather than SQL operations. Tracking changes to the RDS instance itself requires CloudTrail. Publishing database logs to CloudWatch Logs makes them available for monitoring and retention, with metric filters and alarms as needed. Combining native RDS audit logging with CloudWatch Logs and CloudTrail satisfies both database-user auditing and instance-change auditing.
Question 25
A company is building data processing pipelines by using AWS Glue. The pipelines access data stored in Amazon S3. The company has organized the data into folders with prefixes that represent different classification levels. The company needs to restrict AWS Glue jobs to access only specific prefixes based on the data classification. The company must also restrict access to business hours (9 AM to 5 PM).
Which elements must the company include in a custom IAM policy to meet these requirements?
A. A Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the $util.time variable with TimeGreaterThan and TimeLessThan operators
B. A Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the aws:CurrentTime condition key with DateGreaterThan and DateLessThan operators
C. A Condition element that uses the s3:prefix condition key to restrict folder access and aws:CurrentTime with DateGreaterThanEquals and DateLessThanEquals to restrict hours of operation
D. A Condition element that uses the s3:ResourceAccount condition key to restrict bucket access and a Deny statement that applies outside of business hours
Show Answer
Correct Answer: B
Explanation: To restrict access to specific S3 prefixes in an IAM policy, specify S3 object ARN patterns with wildcarded prefixes in the Resource element. Time-based access in IAM uses the global condition key aws:CurrentTime with DateGreaterThan and DateLessThan operators. $util.time is not an IAM policy variable, s3:prefix is primarily for ListBucket requests rather than object access, and s3:ResourceAccount does not restrict prefixes.
Question 26
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%.
Which component of an AWS Glue Data Quality rule will meet these requirements?
A. Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
B. ColumnValues "Email” matches "[%@%.%]" with a threshold set to > 0.1
C. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
D. UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1
Show Answer
Correct Answer: C
Explanation: The AWS Glue Data Quality rule type should validate the values in the Email column against a pattern, not uniqueness. `ColumnValues` with a `matches` regex evaluates the proportion of rows that match the email pattern. Setting the threshold to >0.9 enforces that at least 90% of the column values are valid email addresses; nulls and non-matching values count against the threshold.
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