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 201:

    A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.

    Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

    A. AWS CloudTrail
    B. AWS Health
    C. AWS Trusted Advisor
    D. Amazon CloudWatch
    E. AWS Config

  • Question 202:

    A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.

    A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.

    Which algorithms are best suited to this scenario? (Choose two.)

    A. Latent Dirichlet allocation (LDA)
    B. Random forest classifier
    C. Neural topic modeling (NTM)
    D. Linear support vector machine
    E. Linear regression

  • Question 203:

    A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.

    Which approach will provide the MAXIMUM performance boost?

    A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
    B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
    C. Reduce the learning rate and run the training process until the training loss stops decreasing.
    D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

  • Question 204:

    A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.

    What should the engineer do to improve the validation accuracy of the model?

    A. Perform stratified sampling on the original dataset.
    B. Acquire additional data about the majority classes in the original dataset.
    C. Use a smaller, randomly sampled version of the training dataset.
    D. Perform systematic sampling on the original dataset.

  • Question 205:

    A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1,000 records and 50 features. Prior to training, the ML Specialist notices that two features are perfectly linearly dependent. Why could this be an issue for the linear least squares regression model?

    A. It could cause the backpropagation algorithm to fail during training
    B. It could create a singular matrix during optimization, which fails to define a unique solution
    C. It could modify the loss function during optimization, causing it to fail during training
    D. It could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model

  • Question 206:

    A retail company wants to use Amazon Forecast to predict daily stock levels of inventory. The cost of running out of items in stock is much higher for the company than the cost of having excess inventory. The company has millions of data

    samples for multiple years for thousands of items. The company's purchasing department needs to predict demand for 30-day cycles for each item to ensure that restocking occurs.

    A machine learning (ML) specialist wants to use item-related features such as "category," "brand," and "safety stock count." The ML specialist also wants to use a binary time series feature that has "promotion applied?" as its name. Future

    promotion information is available only for the next 5 days.

    The ML specialist must choose an algorithm and an evaluation metric for a solution to produce prediction results that will maximize company profit. Which solution will meet these requirements?

    A. Train a model by using the Autoregressive Integrated Moving Average (ARIMA) algorithm. Evaluate the model by using the Weighted Quantile Loss (wQL) metric at 0.75 (P75).
    B. Train a model by using the Autoregressive Integrated Moving Average (ARIMA) algorithm. Evaluate the model by using the Weighted Absolute Percentage Error (WAPE) metric.
    C. Train a model by using the Convolutional Neural Network - Quantile Regression (CNN-QR) algorithm. Evaluate the model by using the Weighted Quantile Loss (wQL) metric at 0.75 (P75).
    D. Train a model by using the Convolutional Neural Network - Quantile Regression (CNN-QR) algorithm. Evaluate the model by using the Weighted Absolute Percentage Error (WAPE) metric.

  • Question 207:

    A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data

    Which AWS service should the Data Scientist use?

    A. Amazon Athena
    B. Amazon Redshift Spectrum
    C. AWS Glue
    D. Amazon QuickSight

  • Question 208:

    A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3. Which solution will meet these requirements?

    A. Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.
    B. Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.
    C. Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the IAM role.
    D. Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.

  • Question 209:

    A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.

    Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

    A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
    B. Drop the features with low correlation scores by using a Jupyter notebook.
    C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
    D. Concatenate the features with high correlation scores by using a Jupyter notebook.

  • Question 210:

    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?

    A. 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.
    B. 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.
    C. 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.
    D. 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.

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