A company needs a solution to process streaming data by using Apache Spark in a Kubernetes environment. The solution must support event-driven scaling and optimize resource utilization. The company needs to integrate the solution with existing Kubernetes infrastructure deployed on Amazon Elastic Kubernetes Service (Amazon EKS).
Which solution will meet these requirements with the LEAST operational overhead?
A. Deploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.
B. Use Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event-driven scaling of Spark applications.
C. Use Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.
D. Implement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.
Show Answer
Correct Answer: B
Explanation: Amazon EMR on Amazon EKS provides a managed Apache Spark runtime integrated with existing EKS clusters, reducing operational overhead compared to self-managed Spark. Combining it with Kubernetes Event-Driven Autoscaling (KEDA) enables event-driven scaling for streaming workloads while optimizing resource utilization. The other options either require self-managing Spark or rely on cluster/node scaling rather than event-driven application scaling.
Question 8
A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day.
The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
B. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
C. Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
D. Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS. Use Amazon Kendra for full-text search capabilities.
Show Answer
Correct Answer: B
Explanation: AWS Glue workflows and triggers can be orchestrated by Step Functions, but the key requirements are scheduled daily ETL for legal documents, event/time-based processing for frequently changing lawyer availability, and complex full-text search. Amazon OpenSearch Service is purpose-built for scalable full-text search with low operational overhead. RDS is not suitable for complex full-text search at this scale, and building a custom Lambda search solution increases operational burden. Kendra is aimed at intelligent enterprise search rather than serving as the primary indexed datastore for this ETL/search pipeline.
Question 9
A data engineer needs to deploy a serverless data pipeline. In the pipeline, CSV files are uploaded to an Amazon S3 bucket, which invokes an AWS Lambda function. The Lambda function transforms the CSV files to JSON format and stores the results in a second S3 bucket.
The data engineer has created an AWS Serverless Application Model (AWS SAM) template that includes the Lambda function. The data engineer wants to use AWS SAM for the pipeline deployment.
Which solution will package and deploy this serverless data pipeline?
A. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command to prepare the deployment package. Run the sam deploy --guided command to deploy the pipeline.
B. Run the sam deploy command directly with the --s3-bucket parameter to deploy the Lambda function code. Manually configure the S3 event trigger in the AWS Management Console.
C. Add the first S3 bucket to the SAM template. Run the sam package template to upload the Lambda function code to Amazon S3. Create an AWS CloudFormation stack from the packaged template. Configure event notifications manually.
D. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command followed by the aws cloudformation deploy command to deploy the pipeline.
Show Answer
Correct Answer: A
Explanation: AWS SAM should define all serverless resources, including the S3 bucket event source that triggers the Lambda function. The standard workflow is to run `sam build` to prepare the application and then `sam deploy --guided` (or `sam deploy`) to package, transform, and deploy the SAM application as a CloudFormation stack. The other options either require manual configuration of the event notification, use outdated/incomplete packaging steps, or bypass the normal SAM deployment workflow.
Question 10
A company stores objects in an Amazon S3 bucket. The company crawls the objects so that Amazon Athena can query the data.
A data engineer manually moved all objects from the partition with a path prefix of status=01 to the prefix status=02. The status=01 partition location is now empty. However, the status=01 partition location still appears in the AWS Glue Data Catalog metadata.
Which Athena command should the data engineer run to resolve the metadata discrepancy?
A. MSCK REPAIR TABLE
B. ALTER TABLE DROP PARTITION
C. ALTER TABLE SET TBLPROPERTIES
D. ALTER TABLE CHANGE COLUMN
Show Answer
Correct Answer: B
Explanation: MSCK REPAIR TABLE discovers new partitions but does not remove stale partition metadata. Because the objects were manually moved and the old partition location is now empty, the obsolete partition entry must be removed from the AWS Glue Data Catalog by dropping the partition. ALTER TABLE SET TBLPROPERTIES and CHANGE COLUMN are unrelated.
Question 11
A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.complete_orders_history, which contains 100 columns. The query must return all columns except columns named companyId and unique_system_id.
Which Amazon Redshift SQL statement will meet this requirement?
A.
B.
C.
D.
Show Answer
Correct Answer: A
Explanation: Amazon Redshift supports the SELECT * EXCLUDE syntax to return all columns except specified columns. To exclude companyId and unique_system_id, use: SELECT * EXCLUDE (companyId, unique_system_id) FROM orders.complete_orders_history;. EXCEPT is a set operator that removes rows from one query based on another query, not columns.
Question 12
An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs.
Which solution will meet these requirements?
A. Add additional permissions to the Lambda execution role.
B. Set the log level to ERROR in the Lambda function code.
C. Configure the Lambda function to use the JSON log format.
D. Configure the Lambda function to send logs to a custom log group.
Show Answer
Correct Answer: B
Explanation: For Lambda functions using managed runtimes, application log verbosity can be controlled via log levels. Among the given options, only setting the application log level to ERROR aligns with the requirement to emit only ERROR-level application logs. The other options (IAM permissions, JSON log format, or a custom log group) do not control log severity filtering. Although AWS advanced logging controls support separate application and system log levels, no configuration option for those controls is offered in the choices, making B the best match.
Question 13
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance.
Which solution will meet these requirements?
A. Specify the smaller table on the left side of the join and the larger table on the right side of the join.
B. Specify the larger table on the left side of the join and the smaller table on the right side of the join.
C. Use AWS Glue to pre-process the tables before performing the join.
D. Use table statistics to automatically determine the join order.
Show Answer
Correct Answer: B
Explanation: In Athena (Presto/Trino), the right side of a distributed hash join is typically used as the build side. Placing the smaller table on the right reduces memory usage and improves join performance. Although cost-based optimization can reorder joins when statistics are available, the question asks which join order the engineer should choose when writing the query.
Question 14
A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an Amazon Simple Notification Service (Amazon SNS) topic with an AWS Lambda function subscriber that evaluates the error type and forwards the error to the appropriate email addresses.
B. Configure Amazon EventBridge rules with distinct event patterns for each error type. Route each error type to a dedicated Amazon Simple Notification Service (Amazon SNS) topic for team-specific alerts.
C. Use Amazon Simple Queue Service (Amazon SQS) with message attributes to categorize errors. Allow each team to poll their respective SQS queue for relevant errors.
D. Set up Amazon CloudWatch alarms with different metrics for each error type. Invoke a different Amazon Simple Notification Service (Amazon SNS) notification each time a metrics threshold is crossed.
Show Answer
Correct Answer: B
Explanation: Amazon EventBridge can match events by error type using event patterns and route each type directly to a dedicated Amazon SNS topic for immediate notifications. This avoids custom routing code, provides near real-time event-driven delivery, and has lower operational overhead than using a Lambda router. SQS requires polling and is not immediate, while CloudWatch alarms are intended for metric thresholds rather than per-event error routing.
Question 15
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data.
Which solution will meet this requirement in the MOST operationally efficient way?
A. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.
B. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
C. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.
D. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.
Show Answer
Correct Answer: A
Explanation: AWS Glue DataBrew includes built-in data profiling capabilities designed to analyze datasets stored in Amazon S3, including Parquet files. A profile job generates statistics, schema information, missing values, distributions, and data quality insights with minimal setup, making it the most operationally efficient choice before data preparation. The other options require provisioning or using services that are not intended primarily for data profiling and involve more operational overhead.
Question 16
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: EventBridge can trigger a Step Functions state machine every hour. A Lambda task can poll Amazon RDS, DynamoDB, and OpenSearch Service for new data, and Step Functions can orchestrate the processing with built-in retries, catches, and error handling. This serverless approach has low operational overhead and directly satisfies the explicit requirement for robust error handling better than a simple Lambda+SQS flow.
Sources:
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