Most Recent Amazon MLS-C01 Exam Dumps

Prepare for the Amazon AWS Certified Machine Learning - Specialty exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Amazon MLS-C01 exam and achieve success.

The questions for MLS-C01 were last updated on Apr 21, 2026.

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Question No. 1

[Modeling]

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers

want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored

for offline analysis.

What approach would be the MOST effective to perform near-real time defect detection?

AUse AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from withinAWS IoT Analytics to carry out analysis for anomalies.

BUse Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry outApache Spark ML k-means clustering to determine anomalies.

CUse Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random CutForest (RCF) algorithm to determine anomalies.

DUse Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for furtheranalysis.

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Correct Answer: D

Question No. 2

[Modeling]

An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items

A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.

How should the data scientist meet these requirements MOST cost-effectively?

ATune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:accuracy', 'Type': 'Maximize'}}

BTune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Maximize'}}.

CTune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Maximize'}}.

DTune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Minimize'}).

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Correct Answer: B

Question No. 3

[Data Engineering]

An online store is predicting future book sales by using a linear regression model that is based on past sales dat

a. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.

Which data transformation step should the data scientist take to improve the predictions of the model?

AOne-hot encoding

BCartesian product transformation

CQuantile binning

DNormalization

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Correct Answer: C

Question No. 4

[Modeling]

A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations.

Which solution will meet these requirements with LEAST development effort?

AUse AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

BUse AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.

CUse Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

DUse Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.

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Correct Answer: C

Question No. 5

[Modeling]

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models

During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images

Which of the following should be used to resolve this issue? (Select TWO)

AAdd vanishing gradient to the model

BPerform data augmentation on the training data

CMake the neural network architecture complex.

DUse gradient checking in the model

EAdd L2 regularization to the model

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Correct Answer: B, E

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