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NEW QUESTION # 349
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
Answer:
Explanation:
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 350
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Answer: B
Explanation:
The correct answer is C - Develop an anomaly detection system. According to AWS documentation, anomaly detection models are specifically used to identify unusual or suspicious patterns in data, such as unexpected IP access behavior, unusual network traffic, login anomalies, or deviations from normal usage patterns. Amazon SageMaker and Amazon Lookout for Metrics both support anomaly detection capabilities that detect deviations from learned baselines. AWS highlights that anomaly detection is widely used in cybersecurity, fraud detection, intrusion monitoring, and identifying suspicious IP addresses. Speech recognition (A) and NLP named entity recognition (B) do not classify threat behavior. Fraud forecasting (D) focuses on long-term prediction patterns, not real-time anomaly detection. Since identifying malicious IP sources requires spotting activity outside the normal distribution, anomaly detection is the most accurate and AWS-aligned solution.
Referenced AWS Documentation:
* AWS Machine Learning Specialty Guide - Anomaly Detection Use Cases
* Amazon SageMaker Documentation - Random Cut Forest (RCF) for Anomaly Detection
NEW QUESTION # 351
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
Answer: D
Explanation:
The correct answer is B because explainable AI (XAI) provides transparency into how models reach specific decisions. According to AWS documentation, techniques such as SHAP values (SHapley Additive exPlanations) or LIME can identify which input features (e.g., income, debt ratio, or credit history) most influenced a model's prediction. This helps financial institutions comply with fairness and transparency requirements under regulatory frameworks like the Equal Credit Opportunity Act. AWS SageMaker Clarify is a built-in service that offers explainability reports and bias detection to enhance trust. Rule-based systems and UIs alone do not satisfy transparency standards, and accuracy improvements do not replace explainability. By implementing explainable AI, customers can understand and trust credit limit decisions, reducing bias concerns and ensuring compliance.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Explainability and Feature Attribution AWS Responsible AI Practices - Transparency and Accountability
NEW QUESTION # 352
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
Answer: C
Explanation:
The verified answer is D. Amazon SageMaker Model Monitor. AWS documentation states that Amazon SageMaker Model Monitor monitors the quality of SageMaker AI machine learning models in production. It can set alerts when there are deviations in model quality, enabling early and proactive corrective actions such as retraining models, auditing upstream systems, or fixing quality issues without manually building additional monitoring tooling.
The requirement is to identify changes in original model quality. SageMaker Model Monitor addresses this by creating baselines from training data, computing metrics and constraints, and comparing live or batch inference data against those constraints. AWS documentation states that Model Monitor supports monitoring of data quality, model quality, bias drift, and feature attribution drift. For model quality specifically, it monitors drift in model quality metrics such as accuracy.
Amazon SageMaker JumpStart is incorrect because JumpStart provides prebuilt models, foundation models, notebooks, and solution templates to accelerate ML development. It does not primarily monitor deployed model quality.
Amazon SageMaker HyperPod is incorrect because HyperPod is used for large-scale distributed training infrastructure, especially for foundation models and large ML workloads. It does not identify changes in production model quality.
Amazon SageMaker Data Wrangler is incorrect because Data Wrangler is used for data preparation, transformation, and feature engineering. It helps prepare data before training, but it does not monitor deployed models for quality drift.
Because the company needs to detect changes in model quality after deployment and resolve issues, SageMaker Model Monitor is the correct feature.
NEW QUESTION # 353
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.
Answer:
Explanation:
Explanation:
Sentiment analysis # Text data
Traffic sign recognition # Image data
Customer demographics & purchase history # Tabular data
Stock price forecasting # Time series data
AWS classifies NLP tasks like sentiment analysis under text data
Computer vision tasks such as object and sign recognition use image data Structured rows and columns (demographics, transactions) are tabular data Sequential data indexed by time (prices, metrics) is time series data
NEW QUESTION # 354
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