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Amazon AWS Certified Machine Learning - Specialty Exam
AWS Certified Machine Learning - Specialty (MLS-C01) (Page 4 )

Updated On: 1-Feb-2026
Page 4 of 68
PDF with 370 Questions

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10,000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.

How should the company prepare the data for the model to improve the model's accuracy?

  1. Adjust the class weight to account for each machine type.
  2. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
  3. Undersample the non-failure events. Stratify the non-failure events by machine type.
  4. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Answer(s): B



A data scientist at a retail company is forecasting sales for a product over the next 3 months. After preliminary analysis, the data scientist identifies that sales are seasonal and that holidays affect sales. The data scientist also determines that sales of the product are correlated with sales of other products in the same category.

The data scientist needs to train a sales forecasting model that incorporates this information.

Which solution will meet this requirement with the LEAST development effort?

  1. Use Amazon Forecast with Holidays featurization and the built-in autoregressive integrated moving average (ARIMA) algorithm to train the model.
  2. Use Amazon Forecast with Holidays featurization and the built-in DeepAR+ algorithm to train the model.
  3. Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the SageMaker DeepAR built-in algorithm.
  4. Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the Gluon Time Series (GluonTS) toolkit.

Answer(s): B



A sports broadcasting company is planning to introduce subtitles in multiple languages for a live broadcast. The commentary is in English. The company needs the transcriptions to appear on screen in French or Spanish, depending on the broadcasting country. The transcriptions must be able to capture domain-specific terminology, names, and locations based on the commentary context. The company needs a solution that can support options to provide tuning data.

Which combination of AWS services and features will meet these requirements with the LEAST operational overhead? (Choose two.)

  1. Amazon Transcribe with custom vocabularies
  2. Amazon Transcribe with custom language models
  3. Amazon SageMaker Seq2Seq
  4. Amazon SageMaker with Hugging Face Speech2Text
  5. Amazon Translate

Answer(s): B,E



A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model.

Which change will create the required transformed records with the LEAST operational overhead?

  1. Create an AWS Lambda function that can transform the incoming records. Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target.
  2. Deploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
  3. Deploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to the S3 File Gateway. Use a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3.
  4. Launch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.

Answer(s): A

Explanation:

By creating an AWS Lambda function that can transform the incoming records and enabling data transformation on the ingestion Kinesis Data Firehose delivery stream, the company can transform the data with minimal operational overhead.

The Lambda function can be the invocation target for Kinesis Data Firehose, so that data is transformed as it is ingested.

This approach is serverless and scalable, and it does not require the company to manage any additional infrastructure.


Reference:

https://docs.aws.amazon.com/lambda/latest/dg/lambda-services.html
https://docs.aws.amazon.com/lambda/latest/dg/services-kinesisfirehose.html



A company is building a pipeline that periodically retrains its machine learning (ML) models by using new streaming data from devices. The company's data engineering team wants to build a data ingestion system that has high throughput, durable storage, and scalability. The company can tolerate up to 5 minutes of latency for data ingestion. The company needs a solution that can apply basic data transformation during the ingestion process.

Which solution will meet these requirements with the MOST operational efficiency?

  1. Configure the devices to send streaming data to an Amazon Kinesis data stream. Configure an Amazon Kinesis Data Firehose delivery stream to automatically consume the Kinesis data stream, transform the data with an AWS Lambda function, and save the output into an Amazon S3 bucket.
  2. Configure the devices to send streaming data to an Amazon S3 bucket. Configure an AWS Lambda function that is invoked by S3 event notifications to transform the data and load the data into an Amazon Kinesis data stream. Configure an Amazon Kinesis Data Firehose delivery stream to automatically consume the Kinesis data stream and load the output back into the S3 bucket.
  3. Configure the devices to send streaming data to an Amazon S3 bucket. Configure an AWS Glue job that is invoked by S3 event notifications to read the data, transform the data, and load the output into a new S3 bucket.
  4. Configure the devices to send streaming data to an Amazon Kinesis Data Firehose delivery stream. Configure an AWS Glue job that connects to the delivery stream to transform the data and load the output into an Amazon S3 bucket.

Answer(s): A



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