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Amazon MLS-C01 Exam
AWS Certified Machine Learning - Specialty (MLS-C01) (Page 11 )

Updated On: 1-Feb-2026
Page 11 of 68
PDF with 370 Questions

A data scientist is reviewing customer comments about a company's products. The data scientist needs to present an initial exploratory analysis by using charts and a word cloud. The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model.
Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)

  1. Named entity recognition
  2. Coreference
  3. Stemming
  4. Term frequency-inverse document frequency (TF-IDF)
  5. Sentiment analysis

Answer(s): C,D


Reference:

https://www.analyticsvidhya.com/blog/2020/04/beginners-guide-exploratory-data-analysis-text-data/



A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company’s data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.

The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.

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

  1. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code
    to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
  2. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
  3. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
  4. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.

Answer(s): D


Reference:

https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containersecs-setup.html



A company has an ecommerce website with a product recommendation engine built in TensorFlow. The recommendation engine endpoint is hosted by Amazon SageMaker. Three compute-optimized instances support the expected peak load of the website.

Response times on the product recommendation page are increasing at the beginning of each month. Some users are encountering errors. The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.

Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)

  1. Configure the endpoint to use Amazon Elastic Inference (EI) accelerators.
  2. Create a new endpoint configuration with two production variants.
  3. Configure the endpoint to automatically scale with the InvocationsPerInstance metric.
  4. Deploy a second instance pool to support a blue/green deployment of models.
  5. Reconfigure the endpoint to use burstable instances.

Answer(s): A,C


Reference:

https://aws.amazon.com/machine-learning/elastic-inference/
https://aws.amazon.com/blogs/machine-learning/configuring-autoscaling-inference-endpoints-in-amazon-sagemaker/



A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ТВ of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company’s use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

  1. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.
  2. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.
  3. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
  4. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Answer(s): C

Explanation:

Moving the training data to an Amazon S3 bucket and training and evaluating the model by using Amazon SageMaker will reduce the company's compute infrastructure costs and maximize the scalability of resources for training.

Optimizing the model by using SageMaker Neo will further reduce costs by allowing the model to run on inexpensive edge devices.

Setting up an edge device in the manufacturing facilities with AWS IoT Greengrass and deploying the model on the edge device will enable the company to use the ML model in the low-connectivity environments.

This solution provides a complete end-to-end solution for the company's needs, from data storage to model deployment, while minimizing costs and providing scalability and offline capabilities.



A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model’s accuracy. The learning rate parameter is specified in the following HPO configuration:


During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

  1. Modify the HPO configuration as follows:
    Select the most accurate hyperparameter configuration form this HPO job.
  2. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
    [0.01, 0.1]
    [0.001, 0.01]
    [0.0001, 0.001]
    Select the most accurate hyperparameter configuration form these three HPO jobs.
  3. Modify the HPO configuration as follows:
    Select the most accurate hyperparameter configuration form this training job.
  4. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
    [0.01, 0.1]
    [0.001, 0.01]
    [0.0001, 0.001]
    Select the most accurate hyperparameter configuration form these three HPO jobs.

Answer(s): C



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