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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions
|
| Topic 2: Data Security and Governance | 18% | - Implement access control and authentication
- Enforce compliance and data governance
|
| Topic 3: Data Operations and Support | 22% | - Automate operational tasks - Backup, restore, and disaster recovery - Ensure reliability and scalability - Monitor and troubleshoot data pipelines
|
| Topic 4: Data Ingestion and Transformation | 34% | - Ingest data from various sources
|
>> Data-Engineer-Associate New Study Questions <<
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NEW QUESTION # 207
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.)
Answer: C,E
Explanation:
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 reason why 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.
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
NEW QUESTION # 208
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)
Answer: B,C
Explanation:
Problem Analysis:
The company uses Kinesis Data Streams for both inventory management and reordering.
The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.
Duplicate records were observed in the inventory reordering system.
Key Considerations:
Kinesis streams are designed for durability but may produce duplicates under certain conditions.
Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.
Solution Analysis:
Option A: Network-Related Timeouts
If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
Option B: High IteratorAgeMilliseconds
High iterator age suggests delays in processing but does not directly cause duplication.
Option C: Changes in Shards or Processors
Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
Option D: AggregationEnabled Set to True
AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
Option E: High max_records Value
A high max_records value increases batch size but does not lead to duplication.
Final Recommendation:
Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.
Reference:
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview
NEW QUESTION # 209
A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
Which solution will meet this requirement?
Answer: C
Explanation:
Publishing AWS Glue data quality scores to Amazon DataZone requires creating a DQDL ruleset, scheduling it to run regularly, and then linking the corresponding AWS Glue table as a data source in the DataZone project. The setup ensures that data quality scores from Glue are correctly published and accessible within Amazon DataZone:
"You can define DQDL rulesets for Glue tables and publish the data quality results to DataZone when the project is configured with an AWS Glue data source and the rulesets are scheduled."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Option C follows the expected flow without unnecessary complexity and aligns perfectly with the integration flow supported by AWS.
NEW QUESTION # 210
A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.
Which factors could cause to the permissions-related errors? (Choose two.)
Answer: A,B
Explanation:
QuickSight does not have access to the S3 bucket and QuickSight does not have access to decrypt S3 data are two possible factors that could cause the permissions-related errors. Amazon QuickSight is a business intelligence service that allows you to create and share interactive dashboards based on various data sources, including Amazon Athena. Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. To use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket, you need to grant QuickSight access to both Athena and S3, as well as any encryption keys that are used to encrypt the S3 data. If QuickSight does not have access to the S3 bucket or the encryption keys, it will not be able to read the data from Athena and display it on the dashboard, resulting in an error message that indicates insufficient permissions.
The other options are not factors that could cause the permissions-related errors. Option A, there is no connection between QuickSight and Athena, is not a factor, as QuickSight supports Athena as a native data source, and you can easily create a connection between them using the QuickSight console or the API. Option B, the Athena tables are not cataloged, is not a factor, as QuickSight can automatically discover the Athena tables that are cataloged in the AWS Glue Data Catalog, and you can also manually specify the Athena tables that are not cataloged. Option E, there is no IAM role assigned to QuickSight, is not a factor, as QuickSight requires an IAM role to access any AWS data sources, including Athena and S3, and you can create and assign an IAM role to QuickSight using the QuickSight console or the API. References:
Using Amazon Athena as a Data Source
Granting Amazon QuickSight Access to AWS Resources
Encrypting Data at Rest in Amazon S3
NEW QUESTION # 211
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
Answer: A
Explanation:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using Amazon Timestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
* A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard.
Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream.
These steps can increase the latency and complexity of the solution.
* B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
* D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time.
Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
1: Amazon Managed Streaming for Apache Flink
2: Amazon Timestream
3: Amazon QuickSight
4: Analyze data in Amazon Timestream using Grafana
5: Amazon Kinesis Data Firehose
6: Amazon Aurora
7: AWS Glue Bookmarks
8: AWS Glue Job and Crawler Scheduling
NEW QUESTION # 212
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