Free AWS Certified Machine Learning - Specialty (MLS-C01) Exam Questions and Answers

Last updated: September 12, 2026

Exam code

MLS-C01

Provider

Amazon

Questions in our bank

1000+

Free to read

First 10, with answers

Official page

Official Exam website

Our test mode duration & pass mark

130 mins · 70%

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1. Question #1

   A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers. The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset. Which solution for text extraction and entity detection will require the LEAST amount of effort?

   • A. Extract text from receipt images by using Amazon Textrac
   • B. Use the Amazon SageMaker BlazingTextalgorithm to train on the text for entities and custom entities.
   • C. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplac
   • D. Use the NER deep learning model to extract entities.
   Reveal Answer

2. Question #2

   An Machine Learning Specialist discover the following statistics while experimenting on a model. What can the Specialist from the experiments?

   • A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in
   • B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in
   • C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high
   • D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be
   Reveal Answer

3. Question #3

   A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance. What type of machine learning model should be used?

   • A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
   • B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims ineach category to expect from month to month.
   • C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect frommonth to month.
   • D. Classification with supervised learning of the categories for which partial information on claim contents isprovided, and forecasting using claim IDs and
   Reveal Answer

4. Question #4

   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.
   Reveal Answer

5. Question #5

   A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker. How can the data scientist meet this requirements?

   • A. Call the CreateNotebookInstanceLifecycleConfig API operation
   • B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
   • C. Stop and then restart the SageMaker notebook instance
   • D. Call the UpdateNotebookInstanceLifecycleConfig API operation
   Reveal Answer

6. Question #6

   A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues. The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset. Which feature engineering technique should the Data Scientist use to meet the objectives?

   • A. Run self-correlation on all features and remove highly correlated features
   • B. Normalize all numerical values to be between 0 and 1
   • C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
   • D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
   Reveal Answer

7. Question #7

   A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist. How should the data scientist split the dataset into a training and test set for this use case?

   • A. Shuffle all interaction dat
   • B. Split off the last 10% of the interaction data for the test set.
   • C. Identify the most recent 10% of interactions for each use
   • D. Split off these interactions for the test set.
   Reveal Answer

8. Question #8

   A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements: * Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum. * Support event-driven ETL pipelines. * Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?

   • A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover
   • B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and
   • C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and
   • D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search
   Reveal Answer

9. Question #9

   Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

   • A. Recall
   • B. Misclassification rate
   • C. Mean absolute percentage error (MAPE)
   • D. Area Under the ROC Curve (AUC)
   Reveal Answer

10. Question #10

    A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below. Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values. What technique should be used to convert this column to binary values.

    • A. Binarization
    • B. One-hot encoding
    • C. Tokenization
    • D. Normalization transformation
    Reveal Answer