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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Choose a data store
|
| Data Operations and Support | 22% | - Monitor data pipelines
|
| Data Security and Governance | 18% | - Ensure data encryption
|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
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NEW QUESTION # 220
A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?
Answer: C
Explanation:
* Problem Analysis:
* The company needs to migrate both anapplicationand anon-premises Apache Kafka serverto AWS.
* Incremental updates from an on-premises Oracle database are processed by Kafka.
* The solution must follow areplatform migration strategy, prioritizing minimal changes andlow management overhead.
* Key Considerations:
* Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.
* The solution must provide amanaged Kafka serviceto minimize operational burden.
* Low overhead solutions like serverless services are preferred.
* Solution Analysis:
* Option A: Kinesis Data Streams
* Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.
* This option would require significant application refactoring, which does not align with the replatform strategy.
* Option B: MSK Provisioned Cluster
* Managed Kafka service with fully configurable clusters.
* Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.
* Option C: Amazon Kinesis Data Firehose
* Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.
* Not suitable for Kafka-based applications.
* Option D: MSK Serverless
* MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.
* Automatically scales based on workload, reducing operational overhead.
* Ideal for replatform migrations, as it requires minimal changes to the application.
* Final Recommendation:
* Amazon MSK Serverlessis the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.
:
Amazon MSK Serverless Overview
Comparison of Amazon MSK and Kinesis
NEW QUESTION # 221
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake.
Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Explanation: Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.
NEW QUESTION # 222
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?
Answer: A
Explanation:
In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.
IAM Roles for Service Accounts (IRSA):
With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.
This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.
Reference:
Alternatives Considered:
A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.
C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.
D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long-term credentials manually, which is less secure than using IRSA.
IAM Best Practices for Amazon EKS
Secure Access to DynamoDB from EKS
NEW QUESTION # 223
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?
Answer: C
Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. You can use DataBrew to create recipes that define the steps to apply to your data, such as filtering, renaming, splitting, or aggregating columns. You can also use DataBrew to run jobs that execute the recipes on your data sources, such as Amazon S3, Amazon Redshift, or Amazon Aurora. DataBrew integrates with AWS Glue Data Catalog, which is a centralized metadata repository for your data assets1.
The solution that meets the requirement with the least operational effort is to use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers. This solution has the following advantages:
It does not require you to write any code, as DataBrew provides a graphical user interface that lets you explore, transform, and visualize your data. You can use DataBrew to concatenate the columns that contain customer first names and last names, and then use the COUNT_DISTINCT aggregate function to count the number of unique values in the resulting column2.
It does not require you to provision, manage, or scale any servers, clusters, or notebooks, as DataBrew is a fully managed service that handles all the infrastructure for you. DataBrew can automatically scale up or down the compute resources based on the size and complexity of your data and recipes1.
It does not require you to create or update any AWS Glue Data Catalog entries, as DataBrew can automatically create and register the data sources and targets in the Data Catalog. DataBrew can also use the existing Data Catalog entries to access the data in S3 or other sources3.
Option A is incorrect because it suggests creating and running an Apache Spark job in an AWS Glue notebook. This solution has the following disadvantages:
It requires you to write code, as AWS Glue notebooks are interactive development environments that allow you to write, test, and debug Apache Spark code using Python or Scala. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
It requires you to provision and manage a development endpoint, which is a serverless Apache Spark environment that you can connect to your notebook. You need to specify the type and number of workers for your development endpoint, and monitor its status and metrics.
It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
Option B is incorrect because it suggests creating an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file, and running SQL queries from Amazon Athena to calculate the number of distinct customers. This solution has the following disadvantages:
It requires you to create and run a crawler, which is a program that connects to your data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the Data Catalog. You need to specify the data store, the IAM role, the schedule, and the output database for your crawler.
It requires you to write SQL queries, as Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. You need to use Athena to concatenate the columns that contain customer first names and last names, and then use the COUNT(DISTINCT) aggregate function to count the number of unique values in the resulting column.
Option C is incorrect because it suggests creating and running an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers. This solution has the following disadvantages:
It requires you to write code, as Amazon EMR Serverless is a service that allows you to run Apache Spark jobs on AWS without provisioning or managing any infrastructure. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
It requires you to create and manage an Amazon EMR Serverless cluster, which is a fully managed and scalable Spark environment that runs on AWS Fargate. You need to specify the cluster name, the IAM role, the VPC, and the subnet for your cluster, and monitor its status and metrics.
It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
Reference:
1: AWS Glue DataBrew - Features
2: Working with recipes - AWS Glue DataBrew
3: Working with data sources and data targets - AWS Glue DataBrew
[4]: AWS Glue notebooks - AWS Glue
[5]: Development endpoints - AWS Glue
[6]: Populating the AWS Glue Data Catalog - AWS Glue
[7]: Crawlers - AWS Glue
[8]: Amazon Athena - Features
[9]: Amazon EMR Serverless - Features
[10]: Creating an Amazon EMR Serverless cluster - Amazon EMR
[11]: Using the AWS Glue Data Catalog with Amazon EMR Serverless - Amazon EMR
NEW QUESTION # 224
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
AWS Glue ETL is designed for scalable and serverless data processing, and it supports integrated quality enforcement usingAWS Glue Data Quality, which makes it the most cost-effective and integrated option when combined withAmazon RDS for MySQLas the relational database.
"AWS Glue can perform data validation as part of the ETL process, ensuring data quality before storingthe data in the target data store."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Using AWS Glue Data Quality directly in the ETL workflow is simpler and more cost-effective than separating transformation (Glue) and validation (DataBrew) into different services.
NEW QUESTION # 225
......
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