A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the workflow by using AWS Glue. Configure AWS Glue to begin the third process after the first two processes have finished.
B. Use Amazon EMR to run each process in the workflow. Create an Amazon Simple Queue Service (Amazon SQS) queue to handle messages that indicate the completion of the first two processes. Configure an AWS Lambda function to process the SQS queue by running the third process.
C. Use AWS Glue workflows to run the first two processes in parallel. Ensure that the third process starts after the first two processes have finished.
D. Use AWS Step Functions to orchestrate a workflow that uses multiple AWS Lambda functions. Ensure that the third process starts after the first two processes have finished.
Show Answer
Correct Answer: C
Explanation: AWS Glue workflows natively support long-running, memory-intensive ETL jobs (such as converting CSV to Parquet) with sufficient resource allocation, parallel execution, and dependency management. Glue can easily handle 15 GiB memory jobs, run the first two processes concurrently, and trigger the third only after successful completion. This approach directly fits the use case and minimizes operational overhead compared to managing Airflow, EMR, SQS, Lambda, or Step Functions.
Question 6
A data engineer needs a fully automated solution to check for new data in multiple databases and process data that the solution finds. The solution must run every hour. The solution must be compatible with Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service. The solution must be able to process up to 10 MB of data at one time. The solution must be optimized for costs and operational overhead. The solution must have robust error handling capabilities.
Which solution will meet these requirements?
A. Use Amazon EventBridge to invoke AWS Step Functions every hour to deploy an AWS Lambda function to check for data. Configure Step Functions steps to process data that the Lambda function finds. Implement error handling in each state.
B. Use Amazon EventBridge to invoke an AWS Lambda function every hour to check for data. Configure the function to send a message to an Amazon Simple Queue Service (Amazon SQS) queue when the function finds new data. Use a second Lambda function to read the queue and perform the processing.
C. Configure an Apache Spark application to run on Amazon EMR to check for data. Implement error handling in the application. Use Amazon EventBridge to invoke the application every hour.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create a workflow that runs a directed acyclic graph (DAG) every hour to check for data. Configure the DAG to process identified data. Implement error handling in a Python operator.
Show Answer
Correct Answer: A
Explanation: Option A best meets all requirements. EventBridge provides hourly scheduling, Step Functions offers native orchestration with retries, error handling, and state management, and Lambda supports integration with RDS, DynamoDB, and OpenSearch. The solution is fully serverless, minimizing operational overhead and optimizing costs for an hourly workload and data sizes up to 10 MB. Step Functions is explicitly designed for robust error handling and coordination, which is a stated requirement. Option B lacks built‑in orchestration and robust error handling across multiple steps, while EMR and MWAA introduce unnecessary cost and operational complexity.
Question 7
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format. The company is experiencing slow query performance.
Which solutions will improve query performance? (Choose two.)
A. Use AWS Glue Data Catalog to generate column-level statistics for the Iceberg tables on a schedule.
B. Use AWS Glue Data Catalog to automatically compact the Iceberg tables.
C. Use AWS Glue Data Catalog to automatically optimize indexes for the Iceberg tables.
D. Use AWS Glue Data Catalog to enable copy-on-write for the Iceberg tables.
E. Use AWS Glue Data Catalog to generate views for the Iceberg tables.
Show Answer
Correct Answer: A, B
Explanation: Query performance on Iceberg tables improves mainly by reducing data scanned and optimizing file layout. Generating column-level statistics enables query engines to perform predicate pruning and better query planning. Compaction addresses the small-file problem common with streaming writes, reducing I/O overhead and improving scan efficiency. The other options are not supported Glue/Iceberg features for query optimization or do not materially improve read performance.
Question 7
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the LEAST operational overhead?
A. Assign an IAM role access permissions to the database. Configure the application to obtain temporary credentials through the IAM role.
B. Use AWS Key Management Service (AWS KMS) to generate encryption keys. Configure automatic key rotation. Store the encrypted credentials in an Amazon DynamoDB table.
C. Use AWS Secrets Manager to automatically rotate credentials. Allow the application to retrieve the credentials by using API calls.
D. Store credentials in an encrypted Amazon S3 bucket. Rotate the credentials every month by using an S3 Lifecycle policy. Use bucket policies to control access.
Show Answer
Correct Answer: C
Explanation: AWS Secrets Manager is designed to store and automatically rotate database credentials without requiring application code changes for each rotation. The application retrieves the current credentials via API calls, while Secrets Manager handles rotation securely and with minimal operational overhead. Other options either require code changes, manual rotation processes, or are not intended for database credential management.
Question 8
A data engineer is building a solution to detect sensitive information that is stored in a data lake across multiple Amazon S3 buckets. The solution must detect personally identifiable information (PII) that is in a proprietary data format.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use the AWS Glue Detect PII transform with specific patterns.
B. Use Amazon Made with managed data identifiers.
C. Use an AWS Lambda function with custom regular expressions.
D. Use Amazon Athena with a SQL query to match the custom formats.
Show Answer
Correct Answer: B
Explanation: Amazon Macie is a fully managed service purpose-built to discover and classify sensitive data in Amazon S3 at scale. It supports managed identifiers and custom data identifiers for proprietary PII formats, scans across multiple buckets automatically, and minimizes operational overhead compared to building ETL jobs, custom Lambda regex logic, or ad hoc Athena queries.
Question 8
A company has an Amazon S3 based data lake. The data lake contains datasets that belong to multiple departments. The data lake ingests millions of customer records each day.
A data engineer needs to design an access and storage solution that allows departments to access only the subset of the company's dataset that each department requires. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational effort?
A. Define IAM policies and IAM roles for each department. Specify the S3 access paths from the data lake that each team can access.
B. Set up Amazon Redshift and Amazon Redshift Spectrum as the primary entry points for the data lake. Define an IAM role that Amazon Redshift can assume. Configure the IAM role to grant access to the data that is in Amazon S3.
C. Set up AWS Lake Formation. Assign LF-Tags to AWS Glue Data Catalog resources. Enable Lake Formation tag-based access control (LF-TBAC).
D. Deploy an Amazon RDS for PostgreSQL database that has the aws_s3 extension installed. Configure AWS Step Functions events to invoke an AWS Lambda function to sync the data lake with the database.
Show Answer
Correct Answer: C
Explanation: AWS Lake Formation is purpose-built to manage fine-grained access control for S3-based data lakes with minimal operational overhead. By using the AWS Glue Data Catalog with LF-Tags and tag-based access control (LF-TBAC), the company can centrally define and enforce least-privilege access at the table, column, or database level across departments without managing numerous IAM policies. This scales well for millions of records and multiple teams, unlike raw IAM path policies (A), analytics-specific access via Redshift (B), or complex data replication into RDS (D).
Question 9
A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values:
The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table.
Which solution will meet these requirements?
A. Create a local secondary index (LSI) on DriverID.
B. Create a global secondary index (GSI) that uses RiderID as the partition key and RideStatus as the sort key.
C. Create a global secondary index (GSI) that uses DriverID as the partition key and RideStatus as the sort key.
D. Create a filter expression that uses RiderID and RideStatus.
Show Answer
Correct Answer: C
Explanation: The requirement is to query rides by driver and ride status without scanning a table that has billions of items. DynamoDB requires an index to support efficient queries on non-key attributes. A global secondary index with DriverID as the partition key allows all rides for a given driver to be queried efficiently, and using RideStatus as the sort key enables filtering by status using a key condition instead of a scan. LSIs must share the base table partition key (RideID), which does not support querying by driver. Filter expressions (option D) still require scanning matching partitions and do not avoid full scans. Option B uses RiderID, which does not meet the driver-facing requirement.
Question 9
A company needs to store and analyze a large amount of IoT sensor data. The company needs to retain the data indefinitely. The company analyzes the data in an Amazon Redshift cluster.
Which solution will meet these requirements MOST cost-effectively?
A. Store the data in an Amazon S3 bucket in JSON format. Configure auto-copy data ingestion from the S3 bucket to the Redshift cluster.
B. Store the data in an Amazon S3 bucket in Apache Parquet format. Configure query access through Amazon Redshift Spectrum.
C. Store the data in an Amazon S3 bucket in JSON format. Configure query access through Amazon Redshift Spectrum.
D. Store the data in an Amazon S3 bucket in Apache Parquet format. Configure auto-copy data ingestion from the S3 bucket to the Redshift cluster.
Show Answer
Correct Answer: B
Explanation: For indefinite retention of very large IoT datasets, storing data in Amazon S3 is far cheaper than keeping it inside a Redshift cluster. Using Apache Parquet minimizes storage size and reduces data scanned due to its columnar, compressed format. Amazon Redshift Spectrum allows querying the data directly in S3 without ingesting it into Redshift, avoiding ongoing cluster storage costs and lowering query costs (Spectrum is billed per TB scanned). This combination is the most cost-effective.
Question 10
A company uses a data stream in Amazon Kinesis Data Streams to collect transactional data from multiple sources. The company uses an AWS Glue extract, transform, and load (ETL) pipeline to look for outliers in the data from the stream. When the workflow detects an outlier, it sends a notification to an Amazon Simple Notification Service (Amazon SNS) topic. The SNS topic initiates a second workflow to retrieve logs for the outliers and stores the logs in an Amazon S3 bucket.
The company experiences delays in the notifications to the SNS topic during periods when the data stream is processing a high volume of data. When the company examines Amazon CloudWatch logs, the company notices a high value for the glue.driver.BlockManager.disk.diskSpaceUsed_MB metric when the traffic is high. The company must resolve this issue.
Which solution will meet this requirement with the LEAST operational effort?
A. Increase the number of data processing units (DPUs) in AWS Glue ETL jobs.
B. Use Amazon EMR to manage the ETL pipeline instead of AWS Glue.
C. Use AWS Step Functions to orchestrate a parallel workflow state.
D. Enable auto scaling for the AWS Glue ETL jobs.
Show Answer
Correct Answer: D
Explanation: The high glue.driver.BlockManager.disk.diskSpaceUsed_MB metric indicates that the Glue Spark job is spilling data to disk under heavy load due to insufficient compute and memory resources. Enabling AWS Glue job auto scaling allows Glue to automatically add or remove DPUs based on workload demand, reducing disk spills and processing delays without requiring manual tuning. This directly addresses the performance issue with the least operational effort compared to manually increasing DPUs, re-architecting on EMR, or adding Step Functions orchestration.
Question 10
A data engineer is designing a log table for an application that requires continuous ingestion. The application must provide dependable API-based access to specific records from other applications. The application must handle more than 4,000 concurrent write operations and 6,500 read operations every second.
Which solution will meet these requirements?
A. Create an Amazon Redshift table with the KEY distribution style. Use the Amazon Redshift Data API to perform all read and write operations.
B. Store the log files in an Amazon S3 Standard bucket. Register the schema in AWS Glue Data Catalog. Create an external Redshift table that points to the AWS Glue schema. Use the table to perform Amazon Redshift Spectrum read operations.
C. Create an Amazon Redshift table with the EVEN distribution style. Use the Amazon Redshift Java Database Connectivity (JDBC) connector to establish a database connection. Use the database connection to perform all read and write operations.
D. Create an Amazon DynamoDB table that has provisioned capacity to meet the application's capacity needs. Use the DynamoDB table to perform all read and write operations by using DynamoDB APIs.
Show Answer
Correct Answer: D
Explanation: The workload requires continuous ingestion, very high concurrent write and read throughput, and dependable API-based access to specific records. Amazon DynamoDB is purpose-built for this pattern, offering single-digit millisecond latency, horizontal scalability, and native APIs for key-based reads and writes. With provisioned capacity, DynamoDB can reliably handle thousands of reads and writes per second. Amazon Redshift (options A, B, and C) is optimized for analytical workloads rather than high-concurrency transactional access, and Redshift Spectrum is read-only, making those options unsuitable.
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