Question 1

A healthcare organization is looking to store highly confidential patient records in an Amazon S3 bucket. The storage solution must comply with regulatory standards that require not only encryption of data at rest but also stringent control and auditing over who can access the encryption keys. Since their IT team lacks the resources to manage encryption processes manually, they need an encryption method that is both secure and manageable within their AWS environment.

What encryption method should they use for their S3 bucket to ensure compliance with these standards?

Options :

A : Implement Server-Side Encryption with AWS Key Management Service (SSE-KMS) for encryption at rest and to maintain control over encryption keys

B : Enable Amazon S3 managed keys (SSE-S3) for server-side encryption to ensure data is encrypted at rest with Amazon-managed keys

C : Use Amazon S3 default encryption with an Amazon-managed key for automatic encryption of all stored data.

D : Opt for client-side encryption with a client-managed key for encrypting data before uploading it to the S3 bucket

Answer: A

Question 2

A data analytics team is working with time-sensitive financial data. Their primary dataset resides in an Amazon Redshift cluster, but crucial, frequently updated transactional data is stored in a PostgreSQL database on Amazon RDS. They need a solution to analyze combined data sets in real-time, ensuring that the most current transactional information is included in their analysis without the overhead of continually transferring data between the two systems.

What approach should they consider to efficiently perform these real-time, integrated queries?

Options :

A : Utilize Athena Federated Queries to perform real-time querying across both the Redshift data and the RDS PostgreSQL database.

B : Implement Redshift Spectrum for querying external data, creating external tables that reflect the RDS PostgreSQL data.

C : Regularly export transactional data from RDS PostgreSQL to S3 and use Redshift Spectrum for querying alongside Redshift data.

D : Use Redshift Federated Query to directly integrate and query data from the Redshift cluster and the RDS PostgreSQL database in real-time.

Answer: D

Question 3

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 4

A business specializing in big data analytics is transitioning its data processing from an on-site data center to AWS to cut down on management complexity. They're interested in adopting a serverless architecture wherever possible.

Their current setup involves heavy use of Apache Pig, Apache Oozie, Apache Spark, Apache HBase, and Apache Flink, handling petabytes of data with rapid processing times. The business requires that their new cloud-based solution offer comparable, if not superior, processing performance.

Which AWS service should they choose to achieve serverless ETL at scale?

Options :

A : Use serverless Amazon Athena for interactive query services.

B : Use Amazon S3 with S3 Select for optimized data retrieval.

C : Use Amazon Kinesis Data Analytics for real-time analytics.

D : Use AWS Glue for serverless ETL operations.

Answer: D

Question 5

As a Data Engineer, you've been given the task of enhancing the performance of a business analytics application. This application predominantly performs complex, read-intensive queries against large, historical datasets.

The data is currently housed in a MySQL database on Amazon RDS. However, due to the increasing dataset size and query complexity, query performance has significantly degraded. To address this issue, you are considering implementing Amazon ElastiCache.

Which caching strategy would be most effective in this scenario to improve query performance?

Options :

A : Use ElastiCache for Memcached implementing an Invalidation strategy, where cache entries are invalidated or updated whenever the database write operations occur.

B : Implement ElastiCache for Redis using a Lazy-Loading strategy, loading data into the cache only when it is requested.

C : Utilize ElastiCache for Memcached with a Write-Through caching strategy, ensuring data is written to both the cache and the database simultaneously.

D : Deploy ElastiCache for Redis with a Write-Around caching strategy, where data is written directly to the database and only the data read from the database is cached.

Answer: B