Question 1

A data analytics team is using Amazon Redshift to query a product_table that contains a year column with the format YYYY-MM-DD. They are attempting to retrieve all sales data for the year 2023 using the following SQL query:

SELECT product_name, sum(price)

FROM product_table

WHERE year = 2023

GROUP BY product_name

However, the query does not return any results. What could be the reason for this issue?

Options :

A : An ORDER BY clause needs to be added to the query to properly sort and display the results.

B : The query needs a HAVING clause to include products with non-zero prices: HAVING sum(price) > 0.

C : The WHERE clause is incorrectly filtering the year column; it should use the EXTRACT(YEAR FROM YEAR) function: WHERE EXTRACT(YEAR FROM YEAR) = 2023.

D : The sum(price) function should be replaced with count(*) to count all product occurrences in 2023

Answer: C

Question 2

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 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 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 5

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services. Which solution will meet these requirements with the LEAST management overhead?

Options :

A :

Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.

B :

Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

C : Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

D :

Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

Answer: A

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