A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies. The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)
A. Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.
B. Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.
C. Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
D. Use the Amazon Redshift query optimizer to review and select optimizations to implement.
E. Use Amazon Redshift Advisor to review and select optimizations to implement.
Show Answer
Correct Answer: B, E
Explanation: Using KEY distribution on both tables with a high-cardinality join column colocates matching rows on the same nodes, minimizing data redistribution during the join and improving performance for very large tables. Amazon Redshift Advisor analyzes workload and cluster metadata to recommend concrete performance optimizations (such as distribution keys, sort keys, or query changes), making it an appropriate tool to identify and apply improvements. Other options either increase data movement, use suboptimal distribution styles, or are not a specific optimization mechanism.
Question 11
A company uses AWS Glue ETL pipelines to process data. The company uses Amazon Athena to analyze data in an Amazon S3 bucket.
To better understand shipping timelines, the company decides to collect and store shipping and delivery dates in addition to order data. The company adds a data quality check to ensure that shipping date is greater than order date and that delivery date is greater than shipping date. Orders that fail the quality check must be stored in a second S3 bucket.
Which solution will meet these requirements MOST cost-effectively?
A. Use the AWS Glue DataBrew DATEDIFF function to create two additional columns. Check the new columns.
B. Use Athena to query all three date columns, and compare the columns.
C. Use AWS Glue Data Quality to create a custom rule that uses the three date columns.
D. Use an AWS Glue crawler to populate an AWS Glue Data Catalog. Use the three date columns to create a filter.
Show Answer
Correct Answer: C
Explanation: AWS Glue Data Quality is designed for validating data within Glue ETL pipelines and supports custom rules that compare multiple columns, such as ensuring shipping_date > order_date and delivery_date > shipping_date. It can automatically identify and separate failing records and write them to a different S3 bucket as part of the ETL job. This avoids additional services or query costs, making it the most cost-effective and operationally simple solution.
Question 12
A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level.
The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total.
Which solution will meet these requirements with the LEAST operational cost?
A. Create a standard workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
B. Create an express workflow in AWS Step Functions. Implement a Wait for Callback pattern to wait for the validation steps to finish.
C. Use AWS Lambda functions to implement the workflow. Use Amazon EventBridge to invoke the validation steps.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to implement the workflow.
Show Answer
Correct Answer: A
Explanation: Each transaction can take up to 24 hours end-to-end, which exceeds the maximum execution duration of Step Functions Express workflows (minutes). A Standard Step Functions workflow supports long-running executions (up to a year) and the Wait for Callback pattern allows the workflow to pause without incurring compute cost while validations complete. This results in lower operational cost and simpler orchestration than Lambda/EventBridge or MWAA, which are more complex and expensive for this use case.
Question 12
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics.
Which solution will meet these requirements with the LEAST ongoing maintenance?
A. Use Amazon Data Firehose to ingest the streaming data. Deliver the processed data directly to a provisioned Amazon Redshift cluster.
B. Deploy agents on Amazon EC2 instances to collect the streaming data. Use the AWS CLI to periodically batch upload the data to an Amazon S3 bucket.
C. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.
D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) broker to collect the data. Store the data in an Amazon RDS DB instance.
Show Answer
Correct Answer: C
Explanation: The requirement is real-time clickstream ingestion with the least ongoing maintenance. Amazon Kinesis Data Streams combined with Amazon Data Firehose is a fully managed, serverless AWS-native streaming pipeline that requires minimal operational overhead. Firehose handles scaling, batching, retries, and delivery automatically, and Amazon S3 provides durable, low-cost storage for analytics. The other options require managing infrastructure (EC2 agents, Redshift clusters, MSK brokers, or RDS), which increases maintenance effort.
Question 13
A company stores information about its subscribers in an Amazon S3 bucket. The company runs an analysis every time a subscriber ends their subscription. The company uses AWS Lambda functions to respond to events from the S3 bucket by performing analyses.
The Lambda functions clean data from the S3 bucket and initiate an AWS Glue workflow. The Lambda functions have 128 MB of memory and 512 MB of ephemeral storage. The Lambda functions have a timeout of 15 seconds.
All three functions successfully finish running. However, CPU usage is often near 100%, which causes slow performance. The company wants to improve the performance of the functions and reduce the total runtime of the pipeline.
Which solution will meet these requirements?
A. Increase the memory of the Lambda functions to 512 MB.
B. Increase the number of retries by using the Maximum Retry Attempts setting.
C. Configure the Lambda functions to run in the company's VP
D. Increase the timeout value for the Lambda functions from 15 seconds to 30 seconds.
Show Answer
Correct Answer: A
Explanation: AWS Lambda allocates CPU power proportionally to the configured memory size. The functions are CPU-bound (near 100% CPU) and already complete successfully, but slowly. Increasing the memory from 128 MB to 512 MB will also increase available CPU, improving execution speed and reducing total runtime. The other options do not increase CPU performance.
Question 13
A company generates yearly financial statements for customers and stores the statements in an Amazon S3 bucket. Customers rarely access the documents after 1 week. The company must retain the statements for 7 years. The statements must remain readily accessible for customers.
Which solution will meet these requirements in the MOST cost-effective way?
A. Create an S3 Lifecycle rule to transition objects to S3 Glacier Deep Archive after 7 days. Expire the objects after 7 years.
B. Set the S3 bucket to use S3 Intelligent-Tiering when new objects are uploaded. Set objects to expire after 7 years.
C. Create an S3 Lifecycle rule to transition objects to S3 Glacier Instant Retrieval after 7 days. Expire the objects after 7 years.
D. Set the S3 bucket to use S3 Glacier Instant Retrieval when new objects are uploaded. Create an AWS Lambda function that runs daily to delete any objects that are older than 7 years.
Show Answer
Correct Answer: C
Explanation: Customers rarely access statements after 1 week but must be able to access them immediately for up to 7 years. S3 Glacier Instant Retrieval provides millisecond access like S3 Standard at a much lower storage cost than Standard or Intelligent-Tiering, and unlike Glacier Deep Archive it does not require restore delays. A lifecycle rule can automatically transition objects after 7 days and expire them after 7 years, making option C the most cost-effective and operationally simple solution.
Question 14
A company needs to use Amazon Athena to analyze data that is in an Amazon S3 bucket. A data engineer needs to configure AWS Glue table partitions for year, month, and day. The data engineer needs to create the partitions every day to adjust to schema changes in the data.
Which solution will meet these requirements?
A. Use AWS Glue DataBrew to create the partitions for the AWS Glue table.
B. Use an AWS Lambda function to create the partitions for the AWS Glue table.
C. Set partition projection properties for the AWS Glue table.
D. Configure an AWS Glue crawler to run on a set schedule.
Show Answer
Correct Answer: D
Explanation: A scheduled AWS Glue crawler can automatically discover new S3 partitions (year/month/day) and update the AWS Glue Data Catalog when the schema evolves. This meets the requirement to create partitions daily and adjust to schema changes without manual intervention. Partition projection avoids partition creation but does not address ongoing schema changes in the data.
Question 14
A company needs to build a data pipeline to process a 1-TB file from an Amazon S3 bucket. The pipeline needs to create three DataFrames based on business logic. The pipeline must save all three DataFrames to a second S3 bucket in parallel. The company needs to set the pipeline to be the target of an Amazon EventBridge rule that matches file uploads to the source S3 bucket.
Which solution will meet these requirements with the LEAST maintenance overhead?
A. Configure an Apache Spark Streaming application on Amazon EMR to process data from the S3 source bucket in batches, create DataFrames, and save the output to the destination S3 bucket.
B. Configure three AWS Lambda functions to process the business logic and to save the DataFrames to the destination S3 bucket in parallel.
C. Configure an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
D. Configure an AWS Step Functions state machine to initiate an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
Show Answer
Correct Answer: C
Explanation: AWS Glue workflows can be directly triggered by Amazon EventBridge, process large S3 objects using managed Spark, and orchestrate multiple Glue jobs to run in parallel with minimal operational overhead. This avoids EMR cluster management, exceeds Lambda limits for a 1‑TB file, and does not require an extra Step Functions layer.
Question 15
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account В to process the data from the data stream. The company creates a Lambda execution role in Account В that has permissions to access data from the data stream in Account A.
What additional step must the company take to meet this requirement?
A. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account
B. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.
C. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.
D. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.
Show Answer
Correct Answer: B
Explanation: For cross-account access to Amazon Kinesis Data Streams, the consuming principal must be allowed in the resource owner's account. Even though the Lambda execution role in Account B has IAM permissions, the data stream in Account A must also trust that role. This is done by adding a resource-based policy on the Kinesis data stream in Account A that grants read access to the cross-account Lambda execution role. SCPs cannot grant permissions, and a Lambda resource policy is not used to grant access to Kinesis.
Question 15
A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.
The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.
Which solution will meet these requirements?
A. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values.
B. Use AWS Glue’s Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.
C. Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.
D. Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.
Show Answer
Correct Answer: A
Explanation: AWS Glue DataBrew is designed for no/low‑code data quality checks. It supports built‑in completeness rules to flag missing timestamp values and numeric range validation rules to enforce temperature limits (‑50°C to 150°C). The checks can be defined declaratively and automatically flag bad records, meeting the minimal coding requirement. The other options introduce unnecessary custom code, ML setup, or added service complexity.
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