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A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift.
The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs.
Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)
A. Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).
B. Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.
C. Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.
D. Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.
E. Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.
答案：B,E
解題說明：
To identify the reason why the Step Functions state machine is not able to run the EMR jobs, the company should take the following steps:
Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. The state machine code should have an IAM role that allows it to invoke the EMR APIs, such as RunJobFlow, AddJobFlowSteps, and DescribeStep. The state machine code should also have IAM permissions to access the Amazon S3 buckets that the EMR jobs use as input and output locations. The company can use Access Analyzer for S3 to check the access policies and permissions of the S3 buckets12. Therefore, option B is correct.
Query the flow logs for the VPC. The flow logs can provide information about the network traffic to and from the EMR cluster that is launched in the VPC. The company can use the flow logs to determine whether the traffic that originates from the EMR cluster can successfully reach the data providers, such as Amazon RDS, Amazon Redshift, or other external sources. The company can also determine whether any security group that might be attached to the EMR cluster allows connections to the data source servers on the informed ports. The company can use Amazon VPC Flow Logs or Amazon CloudWatch Logs Insights to query the flow logs3 . Therefore, option D is correct.
Option A is incorrect because it suggests using AWS CloudFormation to automate the Step Functions state machine deployment. While this is a good practice to ensure consistency and repeatability of the deployment, it does not help to identify the reasonwhy the state machine is not able to run the EMR jobs. Moreover, creating a step to pause the state machine during the EMR jobs that fail and wait for a human user to send approval through an email message is not a reliable way to troubleshoot the issue. The company should use the Step Functions console or API to monitor the execution history and status of the state machine, and use Amazon CloudWatch to view the logs and metrics of the EMR jobs .
Option C is incorrect because it suggests changing the AWS Step Functions state machine code to use Amazon EMR on EKS. Amazon EMR on EKS is a service that allows you to run EMR jobs on Amazon Elastic Kubernetes Service (Amazon EKS) clusters. While this service has some benefits, such as lower cost and faster execution time, it does not support all the features and integrations that EMR on EC2 does, such as EMR Notebooks, EMR Studio, and EMRFS. Therefore, changing the state machine code to use EMR on EKS may not be compatible with the existing data pipeline and may introduce new issues.
Option E is incorrect because it suggests checking the retry scenarios that the company configured for the EMR jobs. While this is a good practice to handle transient failures and errors, it does not help to identify the root cause of why the state machine is not able to run the EMR jobs. Moreover, increasing the number of seconds in the interval between each EMR task may not improve the success rate of the jobs, and may increase the execution time and cost of the state machine. Configuring an Amazon SNS topic to store the error messages may help to notify the company of any failures, but it does not provide enough information to troubleshoot the issue.
References:
1: Manage an Amazon EMR Job - AWS Step Functions
2: Access Analyzer for S3 - Amazon Simple Storage Service
3: Working with Amazon EMR and VPC Flow Logs - Amazon EMR
[4]: Analyzing VPC Flow Logs with Amazon CloudWatch Logs Insights - Amazon Virtual Private Cloud
[5]: Monitor AWS Step Functions - AWS Step Functions
[6]: Monitor Amazon EMR clusters - Amazon EMR
[7]: Amazon EMR on Amazon EKS - Amazon EMR
問題 #129
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?
A. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.
B. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
C. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.
D. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
答案：D
解題說明：
Problem Analysis:
Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.
Processing time is increasing due to the additional testing facilities.
The goal is to reduce processing time while using the existing AWS Glue framework.
Key Considerations:
AWS Glue offers the dynamic frame file-grouping feature, which consolidates small files into larger, more efficient datasets during processing.
Grouping smaller files reduces overhead and speeds up processing.
Solution Analysis:
Option A: Lambda for File Grouping
Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.
Option B: AWS Glue Dynamic Frame File-Grouping
This option directly addresses the issue by grouping small files during Glue job execution.
Minimizes data processing time with no extra overhead.
Option C: Redshift COPY Command
COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).
Option D: Amazon EMR
While EMR is powerful, replacing Glue with EMR increases operational complexity.
Final Recommendation:
Use AWS Glue dynamic frame file-grouping for optimized data ingestion and processing.
Reference:
AWS Glue Dynamic Frames
Optimizing Glue Performance
問題 #130
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
B. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
C. Use API calls to access and integrate third-party datasets from AWS
D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
答案：A
解題說明：
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud.
It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
* B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
* C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
* D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
References:
* 1: AWS Data Exchange User Guide
* 2: AWS Data Exchange FAQs
* 3: AWS Glue Developer Guide
* : AWS CodeCommit User Guide
* : Amazon Kinesis Data Streams Developer Guide
* : Amazon Elastic Container Registry User Guide
* : Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source
問題 #131
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10,000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions.
The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Kinesis Producer Library (KPL)
B. Amazon Data Firehose
C. Kinesis Agent
D. Kinesis SDK
答案：A
解題說明：
Problem Analysis:
The company ingests geolocation records (10 bytes each) at 10,000 records per second into Kinesis Data Streams.
Data transmission delays are acceptable, but the solution must maximize throughput efficiency.
Key Considerations:
The Kinesis Producer Library (KPL) batches records and uses aggregation to optimize shard throughput.
Efficiently handles high-throughput scenarios with minimal operational overhead.
Solution Analysis:
Option A: Kinesis Agent
Designed for file-based ingestion; not optimized for geolocation records.
Option B: KPL
Aggregates records into larger payloads, significantly improving shard throughput.
Suitable for applications generating small, high-frequency records.
Option C: Kinesis Firehose
Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.
Option D: Kinesis SDK
The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.
Final Recommendation:
Use Kinesis Producer Library (KPL) for its built-in aggregation and batching capabilities.
Kinesis Producer Library (KPL) Overview
Best Practices for Amazon Kinesis
問題 #132
A data engineer is designing a new data lake architecture for a company. The data engineer plans to use Apache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadata handling. The data engineer needs to query historical data for trend analysis and optimize storage costs for a large volume of event data.
Which solution will meet these requirements with the LEAST development effort?
A. Use AWS Glue Data Catalog to automatically optimize Iceberg storage.
B. Define partitioning schemes based on event type and event date.
C. Run a custom AWS Glue job to compact Iceberg table data files.
D. Store Iceberg table data files in Amazon S3 Intelligent-Tiering.
答案：D
解題說明：
Amazon S3 Intelligent-Tiering is designed to optimize storage costs by automatically moving objects between access tiers based on access patterns. Since Apache Iceberg works with S3 storage, using Intelligent-Tiering provides cost-efficiency without the need for custom development or jobs.
* Option B improves performance but doesn't optimize cost automatically.
* Option C is not a real AWS Glue feature - Glue does not automatically optimize Iceberg storage.
* Option D requires custom development effort, which is contrary to the requirement.
"S3 Intelligent-Tiering is ideal for data lakes and analytics use cases that access data irregularly." Reference: AWS Documentation - S3 Intelligent-Tiering