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Data-Engineer-Associate Practice Questions

AWS Certified Data Engineer - Associate (DEA-C01)

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Question # 1

A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.

The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster ' s logs with the application ' s traces to identify points of failure in the whole application request flow.

Which combination of steps will meet these requirements with the LEAST development effort? (Select TWO.)

Options:

Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.

Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.

Use Amazon OpenSearch to correlate the logs and traces.

Use AWS Glue to correlate the logs and traces.

Discussion 0

Answer:

A, D

Explanation:

Step 1: Log Collection (FluentBit and CloudWatch)

Option A suggests using FluentBit to collect logs and OpenTelemetry to collect traces.

FluentBit is a lightweight log processor that integrates with Amazon EKS to collect and forward logs from Kubernetes clusters. It is widely used with minimal overhead, making it an ideal choice for log collection in this scenario. FluentBit is also natively compatible with AWS services.

OpenTelemetry is a popular framework to collect traces from distributed applications. It provides observability, making it easier to monitor microservices.

This combination allows you to effectively gather both logs and traces with minimal setup and configuration, aligning with the goal of least development effort.

CloudWatch can be used to monitor logs (Option B and C). However, for applications that need more custom and fine-grained control over logging mechanisms, FluentBit and OpenTelemetry are the preferred choice in microservice environments.

Step 2: Log and Trace Correlation (Amazon OpenSearch)

Option D (Amazon OpenSearch) is specifically designed to search, analyze, and visualize logs, metrics, and traces in real-time. OpenSearch allows you to correlate logs and traces effectively.

With Amazon OpenSearch, you can set up dashboards that help in visualizing both logs and traces together, which assists in identifying any failure points across the entire request flow.

It offers integrations with FluentBit and OpenTelemetry, ensuring that both logs from the EKS cluster and application traces are centrally collected, stored, and correlated without additional heavy development.

Step 3: Why Other Options Are Not Suitable

Option B (Amazon Kinesis) is designed for real-time data streaming and analytics but is not as well-suited for tracing microservice requests and logs correlation compared to OpenSearch.

Option C (Amazon MSK) provides a managed Kafka streaming service, but this adds complexity when trying to integrate and correlate logs and traces from a microservice environment. Setting up Kafka requires more development effort compared to using FluentBit and OpenTelemetry.

Option E (AWS Glue) is primarily an ETL (Extract, Transform, Load) service. While Glue is powerful for data processing, it is not a native tool for log and trace correlation, and using it would add unnecessary complexity for this use case.

Conclusion:

To meet the requirements with the least development effort:

Use FluentBit for log collection and OpenTelemetry for tracing (Option A).

Correlate logs and traces using Amazon OpenSearch (Option D).

This approach leverages AWS-native services designed for seamless integration with microservices hosted on Amazon EKS and ensures effective monitoring with minimal overhead.

Question # 2

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?

Options:

Amazon Kinesis Data Streams

Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

Amazon Data Firehose

Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Discussion 0

Question # 3

A retail company is developing a data lake solution on Amazon S3 to analyze historical sales data. The solution needs to support frequent schema changes as new product attributes are added. The company must also be able to query point-in-time historical data snapshots for compliance reporting. The solution must provide atomicity, consistency, isolation, and durability (ACID) transaction guarantees for concurrent write operations.

Which solution will meet these requirements?

Options:

Create an AWS Glue Data Catalog table that uses CSV format. Schedule AWS Glue extract, transform, and load (ETL) jobs to transform the data into Parquet format and partition by date.

Create an AWS Glue Data Catalog table that uses Apache Iceberg table format. Set the format version to 2. Configure time travel retention policies in the table properties.

Enable Amazon S3 Versioning on the company ' s S3 bucket. Create an AWS Glue crawler to catalog the data. Use AWS Glue extract, transform, and load (ETL) jobs to read specific S3 version IDs.

Store the data in Amazon DynamoDB with a composite primary key that includes a timestamp. Use Amazon DynamoDB Streams to capture changes and replicate to Amazon S3 in Parquet format.

Discussion 0

Question # 4

Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket.

Which solution will meet these requirements?

Options:

Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.

Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.

Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.

Discussion 0

Question # 5

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 M

Which solution will meet these requirements MOST cost-effectively?

Options:

Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

Write an AWS Glue Python shell job. Use pandas to transform the data.

Discussion 0

Question # 6

A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.

A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.

The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.

Which solution will meet these requirements?

Options:

Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Change the distribution key to the table column that has the largest dimension.

Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.

Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Discussion 0

Question # 7

A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises servers are the same as the logs that are stored in the S3 bucket.

Which solution will meet this requirement?

Options:

Use the AWS SDK to automatically compute CRC32 checksums during the upload. Store the checksums in S3 object metadata.

Create an AWS Lambda function to calculate SHA-256 checksums. Store the results in a separate metadata table. Validate the logs after the upload.

Enable S3 Object Lock in compliance mode on the S3 bucket. Upload the objects to the bucket.

After uploading the objects to the S3 bucket, enable S3 Object Lock in governance mode on the S3 objects.

Discussion 0

Question # 8

A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.

Options:

Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.

Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.

Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.

Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.

Discussion 0

Question # 9

A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region of an AWS account named Account_

The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_

Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

Options:

Set up an AWS DMS replication instance in Account_B in eu-west-1.

Set up an AWS DMS replication instance in Account_B in eu-east-1.

Set up an AWS DMS replication instance in a new AWS account in eu-west-1.

Set up an AWS DMS replication instance in Account_A in eu-east-1.

Discussion 0

Question # 10

A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university ' s textbooks. The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student ' s selected language.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Build a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queries the database when generating responses with Amazon Bedrock.

Create a knowledge base in Amazon Bedrock Knowledge Bases with the university ' s textbooks. Configure a multilingual model to generate responses with source citations.

Use Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.

Use Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university ' s textbooks to generate personalized feedback with citations.

Discussion 0

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