Question 1

A Cloud Data Engineering Consultant is tasked with setting up a centralized logging solution for multiple AWS Lambda functions used by a client's serverless application. The application has critical real-time processing components, and the engineering consultant must ensure that logging is set up to capture and respond to error conditions rapidly. The logs must be analyzed for specific error patterns, and notifications must be sent out when these patterns are detected.

Which of the following steps should the Cloud Data Engineering Consultant take to establish a robust logging and notification system using Amazon CloudWatch Logs? (Select TWO)

Options :

A : Configure each Lambda function to automatically send execution logs to CloudWatch Logs and set up metric filters to match the specific error patterns.

B : Directly modify each Lambda function-s code to include API calls that send logs to a dedicated CloudWatch Logs group.

C : Set up CloudWatch Alarms based on the metric filters, triggering notifications through Amazon Simple Notification Service (SNS) when error patterns are found.

D : Use Amazon Kinesis Data Firehose to collect logs from Lambda functions and then process them with Amazon CloudWatch for real-time analysis.

E : Implement a custom logging layer within the Lambda functions to batch and push logs asynchronously to an Amazon DynamoDB table for subsequent analysis and alerting.

Answer: A,C

Question 2

A company plans to migrate its on-premises Oracle database to Amazon Aurora PostgreSQL. They need to ensure a seamless migration of both the database schema and the data with minimal downtime. The company also requires a tool to convert the source database schema to be compatible with Amazon Aurora PostgreSQL.

Which combination of AWS services should the company use to migrate the database schema and data?

Options :

A : Utilize Amazon RDS for data migration and AWS Lambda for schema conversion.

B : Use AWS Database Migration Service (DMS) for data migration and AWS Schema Conversion Tool (SCT) for schema conversion.

C : Implement AWS Glue for both schema conversion and data migration.

D : Apply Amazon S3 transfer acceleration for data migration and Amazon Athena for schema conversion.

Answer: B

Question 3

A Data Engineering team at a healthcare analytics company needs to manage complex data pipelines processing sensitive patient data. The pipelines integrate data from hospital records, insurance claims, and clinical trials. As regulatory requirements strictly enforce the accuracy and traceability of data, the team is required to implement a solution that ensures the data's origin, transformations, and usage are transparent and auditable.

Which approach would best ensure the accuracy and trustworthiness of data by enabling comprehensive data lineage?

Options :

A : Implement AWS Glue ETL jobs with metadata tables in AWS Glue Data Catalog, capturing and storing lineage information for all data transformations and movements.

B : Deploy AWS Data Pipeline to orchestrate the data workflows and enable logging in Amazon CloudWatch to monitor data movement and transformation tasks.

C : Use Amazon S3 event notifications to trigger AWS Lambda functions for each data operation, logging every data movement and transformation in Amazon DynamoDB.

D : Utilize Amazon Athena with partition projection to query data directly from Amazon S3, enabling SQL query logging for tracing data access and usage.

Answer: A

Question 4

A Data Engineering team is planning to perform sampling on a large dataset stored in Amazon S3 for exploratory data analysis. Their goal is to understand the overall trends without processing the entire dataset. The dataset is structured and spread across multiple S3 objects.

Which AWS service and method should the team use to implement an efficient and cost-effective sampling strategy?

Options :

A : Implement AWS Glue with a custom Python script to sequentially scan through each S3 object and randomly pick samples from each.

B : Leverage AWS Data Pipeline to orchestrate and move subsets of the data into Amazon RDS, where sampling can be done using SQL queries.

C : Use Amazon S3 Select to efficiently sample individual records from each S3 object, thus reducing the amount of data loaded and processed.

D : Configure Amazon Redshift Spectrum to load the full dataset into a Redshift cluster, and then use SQL queries to perform random sampling.

Answer: C

Question 5

A Cloud Data Engineer is troubleshooting a batch processing workflow where Amazon EC2 instances intermittently fail to process and load transformed data into an Amazon RDS instance. The EC2 instances transform data using a Python script and should handle varying loads efficiently. The Data Engineer observes that the EC2 instances are not scaling as expected, leading to timeouts and failed data loads during peak hours.

Which actions should the Data Engineer take to identify and rectify the scaling issues and prevent future timeouts and load failures? (Select TWO)

Options :

A : Transition to AWS Lambda for data transformation, using its automatic scaling to manage the varying load without manual intervention.

B : Increase the instance size of the EC2 instances to provide more consistent performance under peak loads.

C : Implement Amazon CloudWatch alarms to monitor EC2 CPU utilization and trigger scaling events more effectively.

D : Modify the EC2 Auto Scaling group policies to better match the workload patterns, ensuring that scaling occurs in anticipation of load increases.

E : Optimize the Python data transformation script to decrease the execution time and reduce the processing load on each EC2 instance.

Answer: C,D

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