EXAMS DATA-ENGINEER-ASSOCIATE TORRENT | DATA-ENGINEER-ASSOCIATE PRACTICE GUIDE

Exams Data-Engineer-Associate Torrent | Data-Engineer-Associate Practice Guide

Exams Data-Engineer-Associate Torrent | Data-Engineer-Associate Practice Guide

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q29-Q34):

NEW QUESTION # 29
A company has an application that uses an Amazon API Gateway REST API and an AWS Lambda function to retrieve data from an Amazon DynamoDB instance. Users recently reported intermittent high latency in the application's response times. A data engineer finds that the Lambda function experiences frequent throttling when the company's other Lambda functions experience increased invocations.
The company wants to ensure the API's Lambda function operates without being affected by other Lambda functions.
Which solution will meet this requirement MOST cost-effectively?

  • A. Configure reserved concurrency for the Lambda function.
  • B. Increase the Lambda function timeout and allocated memory.
  • C. Configure provisioned concurrency for the Lambda function.
  • D. Increase the number of read capacity unit (RCU) in DynamoDB.

Answer: A

Explanation:
Reserved concurrencyallows you to dedicate a portion of your account's available concurrency to aspecific Lambda function. This ensures that the function always has sufficient capacity, regardless of how many other functions are running. It's more cost-effective than provisioned concurrency, which reserves warm environments at a higher cost.
"You can use reserved concurrency to guarantee that your critical Lambda functions have the capacity to run as needed without being throttled by other functions."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Provisioned concurrency would be overkill unless latency is also a concern for cold starts, which isn't stated in this case.


NEW QUESTION # 30
A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
  • B. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.
  • C. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
  • D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

Answer: C

Explanation:
This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. Reference:
Amazon Managed Service for Apache Flink
Apache Flink
Window Aggregations in Flink


NEW QUESTION # 31
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.
The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.
A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.
Which solution will meet these requirements with the LEAST operational effort?

  • A. Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.
  • B. Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue.
    Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.
  • C. Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.
  • D. Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: C

Explanation:
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers theleast operational effortby avoiding complex data movement and transformation processes.
* Amazon Athena Federated Queries:
* Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
* The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.
Reference:Amazon Athena Federated Query
Alternatives Considered:
A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.
C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.
D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.
References:
Amazon Athena Documentation
Federated Queries in Amazon Athena


NEW QUESTION # 32
A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?

  • A. Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster
  • B. Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless
  • C. Amazon Data Firehose
  • D. Amazon Kinesis Data Streams

Answer: B

Explanation:
Problem Analysis:
The company needs to migrate both an application and an on-premises Apache Kafka server to AWS.
Incremental updates from an on-premises Oracle database are processed by Kafka.
The solution must follow a replatform migration strategy, prioritizing minimal changes and low management overhead.
Key Considerations:
Replatform Strategy: This approach keeps the application and architecture as close to the original as possible, reducing the need for refactoring.
The solution must provide a managed Kafka service to minimize operational burden.
Low overhead solutions like serverless services are preferred.
Solution Analysis:
Option A: Kinesis Data Streams
Kinesis Data Streams is an AWS-native streaming service but is not a direct substitute for Kafka.
This option would require significant application refactoring, which does not align with the replatform strategy.
Option B: MSK Provisioned Cluster
Managed Kafka service with fully configurable clusters.
Provides the same Kafka APIs but requires cluster management (e.g., scaling, patching), increasing management overhead.
Option C: Amazon Kinesis Data Firehose
Kinesis Data Firehose is designed for data delivery rather than real-time streaming and processing.
Not suitable for Kafka-based applications.
Option D: MSK Serverless
MSK Serverless eliminates the need for cluster management while maintaining compatibility with Kafka APIs.
Automatically scales based on workload, reducing operational overhead.
Ideal for replatform migrations, as it requires minimal changes to the application.
Final Recommendation:
Amazon MSK Serverless is the best solution for migrating the Kafka server and application with minimal changes and the least management overhead.
Reference:
Amazon MSK Serverless Overview
Comparison of Amazon MSK and Kinesis


NEW QUESTION # 33
A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?

  • A. Run the MSCK REPAIR TABLE command from the AWS Glue console.
  • B. Manually run the AWS Glue CreatePartition API twice each day.
  • C. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call.
  • D. Schedule an AWS Glue crawler to run every morning.

Answer: C

Explanation:
The best solution to ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket with the least latency is to use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create partition API call. This way, the Data Catalog is updated as soon as new data is written to S3, and the partition information is immediately available for querying by other services. The Boto3 AWS Glue create partition API call allows you to create a new partition in the Data Catalog by specifying the table name, the database name, and the partition values1. You can use this API call in your code that writes data to S3, such as a Python script or an AWS Glue ETL job, to create a partition for each new S3 object key that matches the partitioning scheme.
Option A is not the best solution, as scheduling an AWS Glue crawler to run every morning would introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. Crawlers can be scheduled to run periodically, such as daily or hourly, but they cannot runcontinuously or in real-time.
Therefore, using a crawler to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency.
Option B is not the best solution, as manually running the AWS Glue CreatePartition API twice each day would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. Moreover, manually running the API would require more operational overhead and human intervention than using code that writes data to S3 to invoke the API automatically.
Option D is not the best solution, as running the MSCK REPAIR TABLE command from the AWS Glue console would also introduce a significant latency between the time new data is written to S3 and the time the Data Catalog is updated. The MSCK REPAIR TABLE command is a SQL command that you can run in the AWS Glue console to add partitions to the Data Catalog based on the S3 object keys that match the partitioning scheme3. However, this command is not meant to be run frequently or in real-time, as it can take a long time to scan the entire S3 bucket and add the partitions. Therefore, using this command to synchronize the Data Catalog with the S3 storage would not meet the requirement of the least latency. References:
AWS Glue CreatePartition API
Populating the AWS Glue Data Catalog
MSCK REPAIR TABLE Command
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 34
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