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Amazon MLS-C01 Dumps PDF
AWS Certified Machine Learning - Specialty- 330 Questions & Answers
- Update Date : June 11, 2026
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Question 1
A data scientist stores financial datasets in Amazon S3. The data scientist uses AmazonAthena to query the datasets by using SQL.The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. Thedata scientist wants to obtain inferences from the model at the SageMaker endpointHowever, when the data …. ntist attempts to invoke the SageMaker endpoint, the datascientist receives SOL statement failures The data scientist's 1AM user is currently unableto invoke the SageMaker endpointWhich combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)
A. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.B. Include a policy statement for the data scientist's 1AM user that allows the 1AM user toperform the sagemaker: lnvokeEndpoint action,
C. Include an inline policy for the data scientist’s 1AM user that allows SageMaker to readS3 objects
D. Include a policy statement for the data scientist's 1AM user that allows the 1AM user toperform the sagemakerGetRecord action.
E. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in theAthena SQL query.
F. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user thatis on the hosted endpoint.
Question 2
A Machine Learning Specialist is designing a scalable data storage solution for AmazonSageMaker. There is an existing TensorFlow-based model implemented as a train.py scriptthat relies on static training data that is currently stored as TFRecords.Which method of providing training data to Amazon SageMaker would meet the businessrequirements with the LEAST development overhead?
A. Use Amazon SageMaker script mode and use train.py unchanged. Point the AmazonSageMaker training invocation to the local path of the data without reformatting the trainingdata.B. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecorddata into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3bucket without reformatting the training data.
C. Rewrite the train.py script to add a section that converts TFRecords to protobuf andingests the protobuf data instead of TFRecords.
D. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue orAWS Lambda to reformat and store the data in an Amazon S3 bucket.
Question 3
A credit card company wants to identify fraudulent transactions in real time. A data scientistbuilds a machine learning model for this purpose. The transactional data is captured andstored in Amazon S3. The historic data is already labeled with two classes: fraud (positive)and fair transactions (negative). The data scientist removes all the missing data and buildsa classifier by using the XGBoost algorithm in Amazon SageMaker. The model producesthe following results:• True positive rate (TPR): 0.700• False negative rate (FNR): 0.300• True negative rate (TNR): 0.977• False positive rate (FPR): 0.023• Overall accuracy: 0.949Which solution should the data scientist use to improve the performance of the model?
A. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class inthe training dataset. Retrain the model with the updated training data.B. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.
C. Undersample the minority class.
D. Oversample the majority class.
Question 4
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolvecritical findings. The company stores audit documents in text format. Auditors haverequested help from a data science team to quickly analyze the documents. The auditorsneed to discover the 10 main topics within the documents to prioritize and distribute thereview work among the auditing team members. Documents that describe adverse eventsmust receive the highest priority. A data scientist will use statistical modeling to discover abstract topics and to provide a listof 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 5
A media company wants to create a solution that identifies celebrities in pictures that usersupload. The company also wants to identify the IP address and the timestamp details fromthe users so the company can prevent users from uploading pictures from unauthorizedlocations.Which solution will meet these requirements with LEAST development effort?
A. Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail tocapture IP address and timestamp details.B. Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWSPanorama Device SDK to capture IP address and timestamp details.
C. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail tocapture IP address and timestamp details.
D. Use Amazon Rekognition to identify celebrities in the pictures. Use the text detectionfeature to capture IP address and timestamp details.
Reviews
Practice material explained scaling ML models using Elastic Inference.
The exam had a detailed case study on text classification with NLP services.
Practice sets covered unsupervised learning like clustering with k-means.
Practice questions helped me understand cross-validation techniques.
Practice questions helped me understand PCA and dimensionality reduction.
The exam included questions about AWS AI services like Rekognition and Translate.
The exam tested workflows using SageMaker Pipelines.
Practice sets explained metrics like precision, recall, and F1 clearly.
Practice sets helped me understand the role of data lakes in ML pipelines.
Questions about cost optimization for ML workloads appeared multiple times.
Data labeling services and Ground Truth were tested directly.
Practice questions really helped me understand feature engineering techniques.
Practice sets included handling imbalanced datasets and SMOTE technique.
Practice questions covered model drift detection clearly.
I faced multiple scenarios on securing ML workloads with IAM roles and encryption.
The exam tested AutoML capabilities in SageMaker Autopilot.
The exam tested building ETL pipelines for ML preprocessing.
Practice sets helped me differentiate between SageMaker and custom frameworks.
Practice questions helped with reinforcement learning basics.
Practice sets explained the differences between batch and real-time inference very well.
The exam tested CI/CD practices for ML deployment.
I had questions about real-time monitoring with CloudWatch for ML workloads.
I had questions on using SageMaker Model Monitor for bias detection.
The MLS-C01 exam had several questions on SageMaker training jobs and deployment.
I had questions about supervised vs unsupervised learning use cases.
I faced questions about managing large datasets in S3 for ML training.
Practice sets explained common algorithms like XGBoost and Linear Learner.
Practice material explained SageMaker Ground Truth labeling tasks well.
Practice sets clarified distributed training techniques in SageMaker.
I had questions about monitoring ML infrastructure costs in AWS.
What are the career benefits of completing the MLS-C01 certification?
Feature selection methods were part of the test questions.
The exam covered deployment security with VPC endpoints and encryption keys.
The exam included scenarios on integrating ML with Lambda functions.
Practice sets explained the benefits of SageMaker Debugger.
The exam covered federated learning in a conceptual manner.
Practice material covered ML pipeline automation in detail.
I had a long case study question on evaluating model performance using confusion matrix.
Practice sets clarified streaming data ingestion for ML workflows.
The exam tested data preprocessing scenarios, including handling missing values.