Question 1

A healthcare application hosted on AWS uses Amazon EC2 instances to manage sensitive patient data that must comply with regulatory security standards. The data is stored on EBS volumes attached to the EC2 instances. The company's security policy mandates that all data at rest be encrypted to protect against unauthorized access.

Which action should the IT department take to secure the EBS volumes in accordance with the company's security policy?

Options :

A : Use AWS Certificate Manager to apply SSL/TLS certificates to the EBS volumes.

B : Encrypt the EBS volumes with Amazon S3 server-side encryption.

C : Enable EBS encryption with AWS Key Management Service (KMS) keys.

D : Run a third-party encryption tool to encrypt data on the EBS volumes before storage.

Answer: C

Question 2

A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has

several use cases. The company must implement permission controls to separate query processes and access to

query history among users, teams, and applications that are in the same AWS account.

Which solution will meet these requirements?

Options :

A :

Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

B :

Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

C :

Create an JAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

D :

Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Answer: B

Question 3

A medium-sized enterprise with fluctuating data analytics demands is exploring Amazon Redshift options. They are looking for a configuration that minimizes the need for active resource management, especially during unpredictable periods of high query volume. The ideal solution should adapt to changing demands while maintaining operational efficiency.

Which Redshift configuration should they consider to meet these requirements of adaptability and ease of management?

Options :

A : Choose Redshift Serverless, as it provides the flexibility to automatically adapt to changing query volumes without manual scaling.

B : Opt for Redshift with RA3 nodes, which offer scalable storage and compute capabilities, and configure auto-scaling policies to handle variable query loads.

C : Select Redshift with On-Demand pricing and set up scaling triggers based on performance metrics to manage varying loads efficiently.

D : Implement a traditional Redshift cluster with dense compute nodes, using dynamic resizing features for adapting to fluctuating workloads.

Answer: A

Question 4

An analytics team at a retail corporation is looking to process their vast amounts of transactional data stored in Amazon S3 using complex analytical queries. The team plans to use Amazon EMR with Apache Hive for their batch processing needs. They require a metadata management system that will allow them to define, organize, and query their data structure.

What component should the team implement within their Amazon EMR environment to manage their metadata effectively and ensure compatibility with Hive?

Options :

A : Use the EMR File System (EMRFS) to handle Hive metadata alongside the transactional data in S3.

B : Deploy an Amazon RDS instance configured as an external Hive Metastore for the EMR cluster.

C : Configure the EMR cluster to use the Glue Data Catalog as a drop-in replacement for the Hive Metastore.

D : Implement Amazon DynamoDB to store and manage Hive metadata for EMR processing tasks.

Answer: C

Question 5

A pharmaceutical company is conducting extensive research and development. Their scientists are running numerous complex computational simulations and bioinformatics algorithms. These tasks are batch processing jobs that can run independently but are computationally intensive.

The company needs to manage these jobs efficiently on AWS, ensuring optimal resource utilization and cost-effectiveness. They also require the ability to schedule jobs, manage dependencies, and handle job retries automatically.

What is the most suitable AWS solution to meet these requirements?

Options :

A : Implement the batch processing using Amazon EMR, with Amazon DynamoDB to manage job queues, and AWS Lambda coupled with Amazon CloudWatch for scheduling and error handling.

B : Set up the computational tasks on Amazon EC2 instances, managed by AWS Elastic Beanstalk for scaling and deployment, using Amazon EventBridge for job scheduling and dependencies.

C : Deploy the computational tasks in Amazon EC2 Auto Scaling groups, utilizing Amazon CloudWatch Events for scheduling and AWS Lambda for dependency management and job retries.

D : Use AWS Batch to manage and run batch jobs, with Amazon S3 for input data storage. Leverage AWS Step Functions to manage job workflows and dependencies.

Answer: D

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