Question 1

Your company's Cloud Data Engineering team is using Amazon Kinesis Data Streams to process real-time streaming data from IoT devices. The stream is configured with 4 shards. Lately, the team observes that during peak operational hours, there are intermittent delays in data processing and some records appear to be dropped. The team is using a custom Kinesis consumer application for data processing.

What initial step should the team take to begin troubleshooting these performance issues?

Options :

A : Inspect the Kinesis Data Streams monitoring metrics in Amazon CloudWatch for incoming record rate and backend processing latencies.

B : Change the data record format to a more compact serialization format like Avro or Protocol Buffers.

C : Adjust the retention period of the data in the stream to allow more time for processing.

D : Increase the shard count in the Kinesis Data Stream to handle higher throughput.

Answer: A

Question 2

A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to minimize the effort and time required to incorporate third-party datasets. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A : Use API calls to access and integrate third-party datasets from AWS Data Exchange.

B : Use API calls to access and integrate third-party datasets from AWS

C :

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.

D :

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Answer: A

Question 3

A Data Engineering Team is tasked with setting up a cloud-based workflow to orchestrate a series of ETL jobs and analytics tasks. They need a solution that requires minimal setup complexity. The workflow includes extracting data from various sources, transforming it using AWS Glue, and loading the results into an Amazon Redshift cluster for querying. The pipeline must manage task dependencies, retries, and error handling efficiently.

Given the need for minimal setup complexity, which AWS service would be most suitable for orchestrating this data pipeline?

Options :

A : Configure a series of Amazon EventBridge (formerly CloudWatch Events) rules to manage the workflow. Each rule would trigger the next process in the ETL pipeline upon successful completion of the previous one.

B : Use AWS Lambda to sequentially trigger each task, where each Lambda function starts the next stage in the pipeline upon the successful completion of the previous one.

C : Implement Amazon Managed Workflows for Apache Airflow (MWAA) to build a robust pipeline. MWAA offers task dependency management, scheduling, and monitoring.

D : Utilize AWS Step Functions to orchestrate the tasks. Step Functions provide a user-friendly interface for visual workflow management, automated retries, error handling, and integration with AWS Glue and Redshift, all with relatively less setup complexity.

Answer: D

Question 4

A company has as JSON file that contains personally identifiable information (PIT) data and non-PII data. The company needs to make the data available for querying and analysis. The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.

B :

Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.

C :

Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permissions to provide access to the required data based on the type of user.

D :

Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.

Answer: C

Question 5

A marketing team at a mid-sized company is looking to streamline its workflow by integrating data from various SaaS applications like Salesforce, Slack, and Google Analytics into their AWS environment for comprehensive analysis. They need a solution that is easy to set up, manage, and capable of securely transferring data between these applications and AWS services without extensive coding.

Which AWS service is most suitable for achieving seamless integration and data flow between their SaaS applications and AWS with LEAST operational overhead?

Options :

A : Employ Amazon Kinesis to stream data from SaaS applications into the AWS environment.

B : Implement AWS Data Pipeline to configure data transfers between each SaaS application and AWS services.

C : Use Amazon AppFlow to create secure and scalable data flows between SaaS applications and AWS services

D : Configure AWS Lambda functions to handle data integration and transfer between SaaS applications and AWS

Answer: C

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