MLS-C01 Exam Details

  • Exam Code
    :MLS-C01
  • Exam Name
    :AWS Certified Machine Learning - Specialty (MLS-C01)
  • Certification
    :Amazon Certifications
  • Vendor
    :Amazon
  • Total Questions
    :396 Q&As
  • Last Updated
    :May 26, 2026

Amazon MLS-C01 Online Questions & Answers

  • Question 271:

    A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.

    The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.

    Which action will MOST likely improve the performance for the forecasting model?

    A. Aggregate sales from stores in the same geographic area.
    B. Apply smoothing to correct for seasonal variation.
    C. Change the forecast frequency from daily to weekly.
    D. Replace missing values in the dataset by using linear interpolation.

  • Question 272:

    An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3. Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )

    A. An AWS KMS key policy that allows access to the customer master key (CMK)
    B. A SageMaker notebook security group that allows access to Amazon S3
    C. An IAM role that allows access to the specific S3 bucket
    D. A permissive S3 bucket policy
    E. An S3 bucket owner that matches the notebook owner
    F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

  • Question 273:

    An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented AI (Amazon A2I).

    Which solution will meet these requirements?

    A. Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.
    B. Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.
    C. Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.
    D. Use AWS Panorama for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

  • Question 274:

    A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested.

    The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.

    Which approach should an ML specialist take to implement the new security feature in the web application?

    A. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.
    B. Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.
    C. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.
    D. Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.

  • Question 275:

    A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package.

    During model training, the data scientist needs to evaluate model performance.

    Which metrics should the data scientist use to meet this requirement? (Choose two.)

    A. InferenceLatency
    B. Mean squared error (MSE)
    C. Root mean squared error (RMSE)
    D. Precision
    E. Accuracy

  • Question 276:

    A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.

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

    A. Use SageMaker Data Wrangler to perform a Gini importance score analysis.
    B. Use a SageMaker notebook instance to perform principal component analysis (PCA).
    C. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
    D. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

  • Question 277:

    A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.

    What should the company do to reduce the processing time of loan applications?

    A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation.
    B. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
    C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation.
    D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

  • Question 278:

    A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.

    The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.

    Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?

    A. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
    B. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
    C. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances. Store the resulting transcriptions in the transcription S3 bucket.
    D. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.

  • Question 279:

    A growing company has a business-critical key performance indicator (KPI) for the uptime of a machine learning (ML) recommendation system. The company is using Amazon SageMaker hosting services to develop a recommendation model

    in a single Availability Zone within an AWS Region.

    A machine learning (ML) specialist must develop a solution to achieve high availability. The solution must have a recovery time objective (RTO) of 5 minutes.

    Which solution will meet these requirements with the LEAST effort?

    A. Deploy multiple instances for each endpoint in a VPC that spans at least two Regions.
    B. Use the SageMaker auto scaling feature for the hosted recommendation models.
    C. Deploy multiple instances for each production endpoint in a VPC that spans least two subnets that are in a second Availability Zone.
    D. Frequently generate backups of the production recommendation model. Deploy the backups in a second Region.

  • Question 280:

    A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.

    Which steps must the data scientist take to improve model accuracy? (Choose three.)

    A. Increase the amount of regularization that the model uses.
    B. Decrease the amount of regularization that the model uses.
    C. Increase the number of training examples that that model uses.
    D. Increase the number of test examples that the model uses.
    E. Increase the number of model features that the model uses.
    F. Decrease the number of model features that the model uses.

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