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: Generating column-level statistics improves query planning, predicate pushdown, and file pruning for Iceberg tables. Automatic compaction reduces the small-file problem common in streaming workloads, decreasing metadata overhead and improving scan efficiency. The other options are not standard Glue Data Catalog features for improving Iceberg query performance or do not directly address query speed.
Question 62
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 the managed service purpose-built to discover sensitive data across Amazon S3. While managed data identifiers detect standard PII, Macie also supports custom data identifiers for proprietary formats, providing the lowest operational overhead compared with building Glue, Lambda, or Athena-based detection. No web search results were provided.
Question 63
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: A GSI with DriverID as the partition key and RideStatus as the sort key allows efficient queries for a specific driver's rides filtered by status without scanning the base table. An LSI cannot be added after table creation and must share the base table's partition key (RideID), so it cannot support queries by DriverID. A GSI on RiderID serves the wrong access pattern. A filter expression still requires reading matching partition data and does not avoid scans for this access pattern.
Question 64
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 the Glue job is constrained during peak workloads, causing spill to disk and slower processing. Enabling AWS Glue Auto Scaling (Glue 3.0+) allows the job to add workers dynamically as load increases, reducing bottlenecks with minimal operational effort. Manually increasing DPUs can help but requires capacity tuning and may overprovision. Migrating to Amazon EMR increases operational overhead, and Step Functions orchestration does not address the ETL processing bottleneck.
Question 65
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: For large joins in Amazon Redshift, colocating rows by using the same KEY distribution on the join column reduces data movement. The distribution key should have high cardinality to avoid skew. Amazon Redshift Advisor provides actionable recommendations for distribution styles, sort keys, and other performance optimizations. The query optimizer is automatic and does not provide recommendations to review and select.
Question 66
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: A Standard AWS Step Functions workflow supports long-running executions (up to 1 year) and the Wait for Callback task token pattern, making it suitable for sequential validation steps that may take many hours but must complete within 24 hours. Express workflows have a much shorter maximum execution duration and are not appropriate for a workflow that may run close to 24 hours. Lambda with EventBridge would require more custom orchestration and operational overhead. Amazon MWAA is unnecessary and more operationally expensive for this sequential transaction workflow.
Question 67
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 ТВ of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Choose two.)
A. Create a key named --conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.
B. Specify the path to a specific version of Iceberg by using the -extra-jars job parameter. Set Iceberg as a value for the datalake-formats job parameter.
C. Set Iceberg as a value for the --datalake-formats job parameter.
D. Set the --enable-auto-scaling parameter to true.
E. Add the --job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.
Show Answer
Correct Answer: C, D
Explanation: To use Apache Iceberg with AWS Glue Spark jobs, configure the job with the --datalake-formats parameter set to Iceberg. For large-scale workloads, enabling AWS Glue auto scaling is the appropriate additional configuration to dynamically allocate workers. The extra-jars option is only needed when using a custom Iceberg version rather than the built-in integration, and job bookmarks are unrelated to Iceberg support.
Question 68
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: Increasing Lambda memory also proportionally increases the allocated CPU. Because the functions are CPU-bound (near 100% CPU utilization) and already complete successfully within the timeout, increasing memory to 512 MB provides more CPU resources, improving execution speed and reducing overall pipeline runtime. Increasing retries, running in a VPC, or increasing the timeout do not address CPU bottlenecks.
Question 69
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 automatically discovers new year/month/day partitions in Amazon S3 and updates the AWS Glue Data Catalog. It can also detect and incorporate schema changes over time. Partition projection avoids catalog partition creation but does not address schema evolution; the requirement explicitly mentions creating partitions daily to adjust to schema changes.
Sources:
https://www.p2pexams.com/free-questions/real-amazon-amazon-dea-c01-study-questions-by-bird.pdf
Question 70
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 a cross-account Lambda event source mapping to read from an Amazon Kinesis Data Stream, permissions are required on both sides: the Lambda execution role in the consuming account needs IAM permissions, and the Kinesis data stream in the owning account must allow the cross-account principal through a resource-based policy. SCPs do not grant permissions, and a Lambda resource policy is not used to authorize Kinesis to read the function.
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