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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
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51. Frage
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.)
Antwort: A,E
Begründung:
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.
Reference:
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
52. Frage
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?
Antwort: D
Begründung:
Option A is the best answer because AWS DMS documentation recommends that, when practical, you create the replication instance in the same Region as your target endpoint, and in the same VPC or subnet as your target endpoint. In this scenario, the target is the Amazon Redshift cluster in eu-west-1 in Account_B, so placing the replication instance there is the most appropriate design. AWS DMS uses the replication instance to connect to the source, read the source data, transform it as needed, and load it into the target.
This also makes architectural sense because the replication instance must have network connectivity to both endpoints, and colocating it with the Redshift target usually simplifies connectivity to the target warehouse and aligns with AWS guidance. A replication instance in Account_B, eu-west-1 can still connect to the PostgreSQL source in Account_A, eu-east-1, provided the required networking and permissions are configured. AWS DMS supports replication tasks by defining a replication instance plus source and target endpoints.
The study guide also identifies AWS DMS as the correct managed service for database migration and continuous replication scenarios, including full load and change data capture. That matches this cross-account, cross-Region migration use case.
53. Frage
A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: A
54. Frage
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.)
Antwort: A,D
Begründung:
The problem likely arises from Athena not being able to read from the correct S3 location or missing partitions. The two most relevant troubleshooting steps involve checking the S3 location and repairing the table metadata.
* A. Confirm that Athena is pointing to the correct Amazon S3 location:
* One of the most common issues with missing data in Athena queries is that the query is pointed to an incorrect or outdated S3 location. Checking the S3 path ensures Athena is querying the correct data.
Reference:Amazon Athena Troubleshooting
C: Use the MSCK REPAIR TABLE command:
When new partitions are added to the S3 bucket without being reflected in the Glue Data Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR TABLE command updates the Glue Data Catalog with the latest partitions.
Reference:MSCK REPAIR TABLE Command
Alternatives Considered:
B (Increase query timeout): Timeout issues are unrelated to missing data.
D (Restart Athena): Athena does not require restarting.
E (Delete and recreate table): This introduces unnecessary overhead when the issue can be resolved by repairing the table and confirming the S3 location.
References:
Athena Query Fails to Return Data
55. Frage
A company needs to build a data lake in AWS. The company must provide row-level data access and column- level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?
Antwort: B
Begründung:
Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.
Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns.
S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.
Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR.
Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.
Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage.
Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* What Is AWS Lake Formation? - AWS Lake Formation
* Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
* Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation
* Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation
* Using Bucket Policies and User Policies - Amazon Simple Storage Service
* Apache Ranger
* Apache Pig
* What Is Amazon Redshift? - Amazon Redshift
56. Frage
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