Question 1

A financial services company is in need of an enhanced storage solution for their transactional relational database system. The solution must ensure high durability, strong consistency, and enhanced read performance for real-time analytics. Additionally, the company needs to maintain high availability and disaster recovery capabilities. Cost management is also a priority, but not at the expense of these performance characteristics. The company's current infrastructure does not require multiple geographical regions.

Which combination of AWS storage services and features should the consultant recommend to meet these requirements? (Select TWO)

Options :

A : Use Amazon DynamoDB with provisioned capacity to manage cost while offering built-in high availability and durability.

B : Implement Amazon EFS with lifecycle management policies to transition older transaction data to EFS Infrequent Access.

C : Utilize Amazon ElastiCache to improve read performance and reduce the load on the primary transaction database.

D : Deploy Amazon Aurora Global Database for high performance, availability, and cross-region disaster recovery.

E : Enable Amazon RDS Multi-AZ deployments for high availability and durability with synchronous data replication.

Answer: C,E

Question 2

A Cloud Data Engineering Team at a data analytics firm is tasked with enhancing their data schema management. The team is dealing with an extensive collection of datasets stored in Amazon S3, with data arriving in varying formats. The requirement is to automate the adaptation of data schemas to reduce the need for manual updates, thereby streamlining analysts' access to the latest data for their complex queries.

Which steps should the team implement to establish a dynamic and automated schema management system that seamlessly accommodates schema evolution? (Select TWO)

Options :

A : Schedule AWS Glue crawlers to automatically detect and incorporate schema modifications into the AWS Glue Data Catalog, accommodating the diverse data formats found in S3 buckets.

B : Develop a bespoke schema tracking application that mandates manual recording and validation of schema changes by data anaysts, aiming for rigorous oversight.

C : Implement AWS Glue Schema Registry within their ETL pipelines to manage schema versioning automatically as data is ingested and transformed, maintaining a record of schema changes.

D : Deploy AWS Glue crawlers for schema detection and an AWS Lambda function for external schema change tracking, using a third-party system instead of AWS-native schema management tools.

E : Manually update te AWS Glue Data Catalog in response to schema change alerts from data ingestion pipelines, ensuring controlled, deliberate schema evolution.

Answer: A,C

Question 3

A Data Engineering Team needs to help the security team to deliver logs from various AWS services in their production AWS account to a centralized logging AWS account for security analysis. They plan to use Amazon Kinesis Data Streams for real-time log processing and delivery.

To ensure secure and efficient log delivery between accounts, where should the Kinesis Data Stream be deployed and what security considerations should be taken into account?

Options :

A : Set up the Kinesis Data Stream in the production account and configure it to push logs directly to the centralized logging account.

B : Implement the Kinesis Data Stream in the production account and use AWS Lambda for cross-account delivery of logs to the centralized logging account.

C : Deploy the Kinesis Data Stream in the centralized logging account and establish cross-account access for the production account to write logs to the stream.

D : Deploy Kinesis Data Streams in both accounts, using one for collecting logs and the other for receiving and processing these logs.

Answer: C

Question 4

A Team of Data Engineers is tasked with developing a serverless data processing pipeline using AWS Lambda functions and Amazon DynamoDB. They want to ensure a smooth deployment process for different environments (development, staging, production) and make their deployment strategy as automated and repeatable as possible. They plan to use the AWS Serverless Application Model (AWS SAM) for this purpose.

Which of the following approaches should they adopt to effectively manage and automate the deployment of their serverless data pipeline across different environments using AWS SAM?

Options :

A : Write a custom script to clone the AWS SAM template and modify resource names and settings for each environment, then deploy using AWS CloudFormation StackSets to manage deployments across multiple AWS accounts and regions.

B : Utilize multiple AWS SAM templates for each component of the pipeline (e.g., one template for Lambda functions, another for DynamoDB tables) and manually deploy each template to appropriate environments using the AWS Management Console.

C : Create separate AWS SAM templates for each environment. Manage environment-specific configurations such as memory size, timeout settings, and DynamoDB throughput settings within each template. Use the AWS CLI to deploy each template to the corresponding AWS environment.

D : Develop a single AWS SAM template and use AWS SAM-s built-in parameters and environment variable configurations to manage different settings for each environment. Automate the deployment process using AWS CodePipeline and AWS CodeBuild, integrating environment-specific parameters during the deployment stage.

Answer: D

Question 5

A digital media company processes large volumes of image and video files daily. These files are stored in an Amazon S3 bucket. The company needs to analyze metadata extracted from these files (such as file size, type, creation date) and store this information for quick retrieval and analysis. The solution should automatically handle new files as they are uploaded and be scalable to handle increasing data volumes.

Which AWS service should the company implement to extract, store, and manage the metadata efficiently?

Options :

A : Use AWS Glue to run ETL jobs on new files in S3, extract metadata, and store it in Amazon RDS for querying.

B : Configure Amazon S3 Event Notifications to trigger AWS Lambda functions for metadata extraction and storage in Amazon DynamoDB.

C : Utilize Amazon Athena to query S3 metadata directly and store the query results in Amazon ElastiCache for quick access.

D : Implement Amazon Kinesis Data Firehose to capture file metadata and load it into Amazon Redshift for analysis.

Answer: B

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