Amazon DATA-ENGINEER-ASSOCIATE Online Practice Questions and Exam Preparation

Amazon DATA-ENGINEER-ASSOCIATE Online Questions & Answers

• Question 21:

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

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

  A. Set up an AWS DMS replication instance in Account_B in eu-west-1.
  B. Set up an AWS DMS replication instance in Account_B in eu-east-1.
  C. Set up an AWS DMS replication instance in a new AWS account in eu-west-1
  D. Set up an AWS DMS replication instance in Account_A in eu-east-1.
  A. Set up an AWS DMS replication instance in Account_B in eu-west-1.

• Question 22:

  How to trigger a Lambda function only when a .

  csv file is uploaded to an S3 bucket with minimal overhead?

  A. Create an S3 event notification for s3:ObjectCreated:* with a .csv suffix filter and a Lambda ARN as the destination
  B. Use the s3:ObjectTagging:* event for objects tagged as .csv
  C. Use the s3:* event with a .csv suffix filter
  D. Send S3 events to Amazon SNS and subscribe the Lambda function to the topic
  A. Create an S3 event notification for s3:ObjectCreated:* with a .csv suffix filter and a Lambda ARN as the destination

• Question 23:

  A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.

  The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to useData Quality Definition Language (DQDL)rules in an AWS Glue Data Quality job.

  Which rule will meet these requirements?

  A. DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0
  B. ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0
  C. DatasetMatch "reference" "city->ref_city, state->ref_state" = 100
  D. ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100
  A. DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

• Question 24:

  A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule.

  Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.

  The company must ensure that the application performs consistently during peak usage times.

  Which solution will meet these requirements in the MOST cost-effective way?

  A. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
  B. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
  C. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.Schedule lower capacity during off-peak times.
  D. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.
  C. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.Schedule lower capacity during off-peak times.

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  Explanation

  Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB offers two capacity modes for throughput capacity: provisioned and on-demand. In provisioned capacity mode, you specify the number of read and write capacity units per second that you expect your application to require. DynamoDB reserves the resources to meet your throughputneeds with consistent performance. In on-demand capacity mode, you pay per request and DynamoDB scales the resources up and down automatically based on the actual workload.

  On-demand capacity mode is suitable for unpredictable workloads that can vary significantly over time.

  The solution that meets the requirements in the most cost-effective way is to use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times and lower capacity during off-peak times. This solution has the following advantages: It allows you to optimize the cost and performance of your DynamoDB table by adjusting the provisioned capacity according to your predictable workload patterns. You can use scheduled scaling to specify the date and time for the scaling actions, and the new minimum and maximum capacity limits. For example, you can schedule higher capacity for every Monday morning and lower capacity for weekends. It enables you to take advantage of the lower cost per unit of provisioned capacity mode compared to on-demand capacity mode. Provisioned capacity mode charges a flat hourly rate for the capacity you reserve, regardless of how much you use. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode. It ensures that your application performs consistently during peak usage times by having enough capacity to handle the increased load. You can also use auto scaling to automatically adjust the provisioned capacity based on the actual utilization of your table, and set a target utilization percentage for your table or global secondary index. This way, you can avoid under-provisioning or over-provisioning your table.

  Option A is incorrect because it suggests increasing the provisioned capacity to the maximum capacity that is currently present during peak load times. This solution has the following disadvantages: It wastes money by paying for unused capacity during off-peak times. If you provision the same high capacity for all times, regardless of the actual workload, you are over-provisioning your table and paying for resources that you don't need. It does not account for possible changes in the workload patterns over time. If your peak load times increase or decrease in the future, you may need to manually adjust the provisioned capacity to match the new demand. This adds operational overhead and complexity to your application.

  Option B is incorrect because it suggests dividing the table into two tables and provisioning each table with half of the provisioned capacity of the original table. This solution has the following disadvantages: It complicates the data model and the application logic by splitting the data into two separate tables. You need to ensure that the queries are evenly distributed acrossboth tables, and that the data is consistent and synchronized between them. This adds extra development and maintenance effort to your application.

  It does not solve the problem of adjusting the provisioned capacity according to the workload patterns. You still need to manually or automatically scale the capacity of each table based on the actual utilization and demand. This may result in under-provisioning or over-provisioning your tables.

  Option D is incorrect because it suggests changing the capacity mode from provisioned to on-demand.

  This solution has the following disadvantages: It may incur higher costs than provisioned capacity mode for predictable workloads. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode, as you can reserve the capacity you need at a lower rate. It may not provide consistent performance during peak usage times, as on-demand capacity mode may take some time to scale up the resources to meet the sudden increase in demand. On-demand capacity mode uses adaptive capacity to handle bursts of traffic, but it may not be able to handle very large spikes or sustained high throughput. In such cases, you may experience throttling or increased latency.

  References:

  1: Choosing the right DynamoDB capacity mode - Amazon DynamoDB

  2: Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

  3: Best practices for designing and using partition keys effectively - Amazon DynamoDB [4]: On-demand mode guidelines - Amazon DynamoDB [5]: How to optimize Amazon DynamoDB costs - AWS Database Blog [6]: DynamoDB adaptive capacity: How it works and how it helps - AWS Database Blog [7]: Amazon DynamoDB pricing - Amazon Web Services (AWS)

• Question 25:

  An application retrieves database credentials for an ETL job. Security requirements state that credentials must be rotated automatically and must not be stored in code or plain-text job parameters.

  Which design choices meet these requirements? (Choose two.)

  A. Store the credentials in AWS Secrets Manager.
  B. Configure automatic rotation for the secret with an appropriate rotation function and schedule.
  C. Place the password in the Glue job's default arguments so every run can read it.
  D. Commit the encrypted password string directly into the ETL source repository.
  E. Share the password through an S3 object with public read access.
  A. Store the credentials in AWS Secrets Manager.
  B. Configure automatic rotation for the secret with an appropriate rotation function and schedule.

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  Explanation

  Secrets Manager is designed to store secrets securely, and automatic rotation can update supported database credentials on a schedule. Job arguments, source code, and public S3 objects expose secrets and make rotation harder to control safely.

• Question 26:

  A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics.

  Which solution will meet these requirements with the LEAST ongoing maintenance?

  A. Use Amazon Data Firehose to ingest the streaming data. Deliver the processed data directly to a provisioned Amazon Redshift cluster.
  B. Deploy agents on Amazon EC2 instances to collect the streaming data. Use the AWS CLI to periodically batch upload the data to an Amazon S3 bucket.
  C. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.
  D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) broker to collect the data. Store the data in an Amazon RDS DB instance.
  C. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.

• Question 27:

  A data engineer is troubleshooting an AWS Glue workflow that occasionally fails. The engineer determines that the failures are a result of data quality iues. A busine reporting team needs to receive an email notification any time the workflow fails in the future.

  Which solution will meet this requirement?

  A. Create an Amazon Simple Notification Service (Amazon SNS) FIFO topic. Subscribe the team's email account to the SNS topic. Create an AWS Lambda function that initiates when the AWS Glue job state changes to FAILED. Set the SNS topic as the target.
  B. Create an Amazon Simple Notification Service (Amazon SNS) standard topic. Subscribe the team's email account to the SNS topic. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILED. Set the SNS topic as the target.
  C. Create an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Subscribe the team's email account to the SQS queue. Create an AWS Config rule that triggers when the AWS Glue job state changes to FAILED. Set the SQS queue as the target.
  D. Create an Amazon Simple Queue Service (Amazon SQS) standard queue. Subscribe the team's email account to the SQS queue. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILED. Set the SQS queue as the target.
  B. Create an Amazon Simple Notification Service (Amazon SNS) standard topic. Subscribe the team's email account to the SNS topic. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILED. Set the SNS topic as the target.

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  Explanation

  Amazon EventBridge can detect AWS Glue job state change events natively. You configure a rule that matches when a job's state transitions to FAILED and set a standard Amazon SNS topic as the target. By subscribing the busine reporting team's email addrees to that SNS topic, they'll receive automatic email notifications whenever the workflow fails, with no custom polling or Lambda required.

• Question 28:

  A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.

  Which solution will meet this requirement?

  A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
  B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
  C. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
  D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
  B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

• Question 29:

  A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation.

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

  A. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
  B. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.
  C. Deploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.
  D. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
  B. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.

• Question 30:

  A company needs to implement real-time analytics for a retail shopping platform. The company wants to capture clickstream data, process the data, and load the data into Amazon Redshift for analysis. The solution must handle hundreds of megabytes of data every second.

  Which solution will meet these requirements with the LEAST query latency for analytics?

  A. Use Amazon Data Firehose to capture the data. Store the data in an Amazon S3 bucket. Use the COPY command to load data into Amazon Redshift.
  B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to capture the data. Use Amazon EMR to process the data. Use federated queries to access data in Amazon Redshift.
  C. Use Amazon Kinesis Data Streams to capture the data. Use Amazon Redshift streaming ingestion to load data directly into materialized views.
  D. Use Amazon DynamoDB Streams to capture the data. Use AWS Glue to process the data. Use a zero-ETL integration to load the data into Amazon Redshift.
  C. Use Amazon Kinesis Data Streams to capture the data. Use Amazon Redshift streaming ingestion to load data directly into materialized views.