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 company stores its processed data in an S3 bucket. The company has a strict data access policy. The

company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.

The company wants to receive notifications when a user violates the data access policy. Each notification

must include the username of the user who violated the policy.

Which solution will meet these requirements?

Options :

A : Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.

B : Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.

C :

Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

D :

Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Answer: C

Question 3

A Cloud Data Engineering Team is responsible for maintaining a large-scale application hosted on AWS. The team needs to analyze the application's logs efficiently to monitor performance and identify issues.

These logs are currently stored in JSON format in an Amazon S3 bucket and are frequently updated. The team wants to leverage a service that can query these logs directly from S3 with minimal setup and maintenance, allowing them to quickly derive insights and patterns.

Which AWS service should they choose for this purpose?

Options :

A : Configure Amazon EMR with Apache Spark to process and analyze the log data stored in S3.

B : Set up an Amazon OpenSearch Service domain to index the log data, enabling powerful search and analysis capabilities.

C : Use Amazon Athena to directly query the logs in S3 using SQL-like queries without the need for loading data into a separate analytics tool.

D : Deploy a custom log analysis solution on Amazon EC2 instances, utilizing tools like Elasticsearch for querying and analysis.

Answer: C

Question 4

A Data Engineering Team is working on a project that involves extracting data from multiple databases, including both SQL and NoSQL sources, into an AWS Glue job for further processing. They need to ensure that their connections to these diverse databases are efficient and secure.

Which of the following approaches is most appropriate for establishing these connections?

Options :

A : Directly embed database credentials in the AWS Glue ETL script and use native libraries of the respective databases for establishing connections.

B : Use AWS Glue-s built-in connectors for JDBC and ODBC, ensuring database credentials are stored securely in AWS Secrets Manager and accessed via Glue scripts.

C : Configure AWS Direct Connect for each database source to ensure a dedicated, private connection between the databases and AWS Glue.

D : Utilize Amazon VPC peering connections for each database, and manage authentication through network-level security without needing individual credentials.

Answer: B

Question 5

You are a Data Engineer tasked with architecting a solution for an online retail application on AWS. The application must efficiently manage user shopping cart data (stateful transactions) and handle rapid, high-volume event logging for website interactions, like clicks and views (stateless transactions).

Considering the need for high availability, scalability, and near real-time processing, which of the following AWS service combinations is the most appropriate for handling these specific transaction types?

Options :

A : Use Amazon DynamoDB with Global Tables for the shopping cart data to enable stateful transactions across multiple regions, and AWS Lambda combined with Amazon Kinesis Data Firehose for processing the high-volume event logs as stateless transactions.

B : Deploy Amazon RDS with Read Replicas for handling the shopping cart-s stateful transactions, and Amazon S3 with S3 Event Notifications to manage the stateless transactions of event logs.

C : Implement Amazon Aurora Serverless for the shopping cart data, facilitating stateful transactions with auto-scaling capabilities, and deploy Amazon EC2 instances behind an Elastic Load Balancer for processing the stateless transactions of event logging.

D : Utilize Amazon ElastiCache for Redis to manage the stateful transactions of shopping cart data, and Amazon Managed Streaming for Apache Kafka (Amazon MSK) for the high-throughput processing of stateless event log transactions.

Answer: A

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