AWS MLS C01 AWS Certified Machine Learning Specialty Dumps

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Here are 10 multiple-choice review questions with explanations for the AWS Certified Machine Learning Specialty (MLS-C01) exam:

1. Which AWS service is best suited for real-time inference with low latency requirements?

A) Amazon SageMaker Batch Transform

B) AWS Lambda

C) Amazon SageMaker Real-Time Inference

D) Amazon KinesisAnswer: C) Amazon SageMaker Real-Time Inference

Explanation: SageMaker Real-Time Inference provides low-latency predictions using endpoint deployments, making it ideal for real-time applications. Batch Transform (A) is for offline processing, Lambda (B) is not optimized for ML inference, and Kinesis (D) is for streaming data.

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2. What is the purpose of Amazon SageMaker Ground Truth?

A) To train deep learning models

B) To automatically label large datasets using human annotators or automated labeling

C) To deploy ML models in production

D) To visualize model performanceAnswer: B) To automatically label large datasets using human annotators or automated labeling

Explanation: SageMaker Ground Truth helps create high-quality labeled datasets by combining human and automated labeling workflows.

3. Which algorithm is best for a supervised learning problem with tabular data and categorical features?

A) K-Means

B) XGBoost

C) Principal Component Analysis (PCA)

D) Latent Dirichlet Allocation (LDA)Answer: B) XGBoost

Explanation: XGBoost is a powerful gradient-boosting algorithm for structured/tabular data. K-Means (A) is unsupervised, PCA (C) is for dimensionality reduction, and LDA (D) is for topic modeling.

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4. How does Amazon SageMaker Automatic Model Tuning (Hyperparameter Optimization) work?

A) It manually tests different hyperparameters based on user input.

B) It uses Bayesian optimization to find the best hyperparameters.

C) It only supports random search for hyperparameter tuning.

D) It requires predefined hyperparameter values.Answer: B) It uses Bayesian optimization to find the best hyperparameters.

Explanation: SageMaker Automatic Model Tuning uses Bayesian optimization (and optionally random search) to efficiently find optimal hyperparameters.

5. Which AWS service is used for feature engineering and preprocessing at scale?

A) Amazon SageMaker Processing

B) Amazon Athena

C) Amazon QuickSight

D) AWS GlueAnswer: A) Amazon SageMaker Processing

Explanation: SageMaker Processing is designed for distributed data preprocessing and feature engineering. Athena (B) is for SQL queries, QuickSight (C) is for BI, and Glue (D) is for ETL.

6. What is the purpose of SHAP (SHapley Additive exPlanations) in ML models?

A) To improve model accuracy

B) To explain model predictions by attributing feature importance

C) To compress model size

D) To deploy models fasterAnswer: B) To explain model predictions by attributing feature importance

Explanation: SHAP values help interpret ML models by quantifying each feature's contribution to predictions.

7. When should you use Amazon SageMaker Neo?

A) To optimize ML models for deployment on specific hardware

B) To label training data

C) To perform hyperparameter tuning

D) To visualize training metricsAnswer: A) To optimize ML models for deployment on specific hardware

Explanation: SageMaker Neo compiles and optimizes models for edge devices or cloud instances, improving inference performance.

8. Which AWS service is best for building a recommendation system?

A) Amazon Personalize

B) Amazon Rekognition

C) Amazon Comprehend

D) Amazon ForecastAnswer: A) Amazon Personalize

Explanation: Amazon Personalize is a managed service for building real-time recommendation engines. Rekognition (B) is for computer vision, Comprehend (C) is for NLP, and Forecast (D) is for time-series predictions.

9. What is the role of a validation dataset in model training?

A) To train the model initially

B) To evaluate model performance during training and prevent overfitting

C) To deploy the model in production

D) To store raw dataAnswer: B) To evaluate model performance during training and prevent overfitting

Explanation: The validation set helps tune hyperparameters and detect overfitting before final evaluation on the test set.

10. Which AWS service provides built-in anomaly detection algorithms?

A) Amazon SageMaker Clarify

B) Amazon Lookout for Metrics

C) Amazon Fraud Detector

D) Amazon KendraAnswer: B) Amazon Lookout for Metrics

Explanation: Lookout for Metrics automatically detects anomalies in business metrics using ML. Fraud Detector (C) is for fraud detection, while Clarify (A) is for bias detection and Kendra (D) is a search service.

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Final Tips for AWS MLS C01 AWS Certified Machine Learning Specialty Dumps:

• Focus on SageMaker (training, inference, tuning, processing).
• Understand AWS AI services (Personalize, Forecast, Rekognition, etc.).
• Know model evaluation techniques (precision/recall, ROC, SHAP).
• Review data preprocessing and feature engineering best practices.

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