Amazon MLS-C01 practice materials Questions Answers

Exam Code:
MLS-C01
Exam Name:
AWS Certified Machine Learning – Specialty Exam
Last Update:
Jun 01,2026

330 Questions Answers Verified by Experts!


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Amazon MLS-C01 Questions for Amazon Specialty Certification Exam 2026

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Page: 1 / 62
Total Questions: 307

Questions & Answers PDF

• Question 1

  

  A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

  • Use Amazon SageMaker Ground Truth to sort the data into two groups named 'enrolled' or 'not enrolled.'
  • Use a forecasting algorithm to run predictions.
  • Use a regression algorithm to run predictions.
  • Use a classification algorithm to run predictions
  • Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named 'enrolled' or 'not enrolled.'

  Reveal Answer Answer: A, D Next Question

• Question 2

  

  A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns onlyHow should the Machine Learning Specialist transform the dataset to minimize query runtime?

  • Convert the records to Apache Parquet format
  • Convert the records to JSON format
  • Convert the records to GZIP CSV format
  • Convert the records to XML format

  Reveal Answer Answer: A Next Question

• Question 3

  

  A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactionsWhat metric is BEST suited to score the model?

  • Precision
  • Recall
  • Area Under the ROC Curve (AUC)
  • Root Mean Square Error (RMSE)

  Reveal Answer Answer: C Next Question

• Question 4

  

  A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)

  • Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
  • Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
  • Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
  • Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.
  • Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.

  Reveal Answer Answer: A, B Next Question

• Question 5

  

  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.)

  • Latent Dirichlet allocation (LDA)
  • Random Forest classifier
  • Neural topic modeling (NTM)
  • Linear support vector machine
  • Linear regression

  Reveal Answer Answer: A, C Next Question

• Question 6

  

  A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customerbase. The dataset contains thousands of columns of data and hundreds of numerical columns for eachcustomer. The Specialist wants to identify whether there are natural groupings for these columns across allcustomers and visualize the results as quickly as possible.What approach should the Specialist take to accomplish these tasks?

  • Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
  • Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.
  • Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
  • Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

  Reveal Answer Answer: A Next Question

• Question 7

  

  A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?

  • Logistic regression
  • Random Cut Forest (RCF)
  • Principal component analysis (PCA)
  • Linear regression

  Reveal Answer Answer: D Next Question

• Question 8

  

  A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

  • Receiver operating characteristic (ROC) curve
  • Misclassification rate
  • Root Mean Square Error (RM&)
  • L1 norm

  Reveal Answer Answer: A Next Question

• Question 9

  

  A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical data. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.Which solution will meet these requirements with the LEAST development effort?

  • Upload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.
  • Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.
  • Upload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.
  • Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.

  Reveal Answer Answer: B Next Question

• Question 10

  

  A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.Which cross-validation strategy should the Data Scientist adopt?

  • A k-fold cross-validation strategy with k=5
  • A stratified k-fold cross-validation strategy with k=5
  • A k-fold cross-validation strategy with k=5 and 3 repeats
  • An 80/20 stratified split between training and validation

  Reveal Answer Answer: B Next Question

Page: 1 / 62
Total Questions: 307

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