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Amazon AWS Certified AI Practitioner Sample Questions (Q184-Q189):
NEW QUESTION # 184
An AI practitioner has trained a model on a training dataset. The model performs well on the training data. However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?
Answer: B
Explanation:
When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.
* Overfitting: The model memorizes the training data patterns instead of generalizing.
* Underfitting (A) means the model performs poorly on both training and test data.
* Bias (C) refers to systemic errors in predictions, not this training/test mismatch.
* Prompt engineering (B) applies to generative AI, not general ML training models.
# Reference:
AWS ML Glossary - Overfitting and Underfitting
NEW QUESTION # 185
An ecommerce company is developing a generative Al solution to create personalized product recommendations for its application users. The company wants to track how effectively the Al solution increases product sales and user engagement in the application.
Select the correct business metric from the following list for each business goal. Each business metric should be selected one time. (Select THREE.) Average order value (AOV) Click-through rate (CTR) Retention rate
Answer:
Explanation:
NEW QUESTION # 186
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: D
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 # 187
What is the benefit of fine-tuning a foundation model (FM)?
Answer: C
Explanation:
Comprehensive and Detailed Explanation from AWS AI Documents:
Fine-tuning a foundation model means taking a pre-trained large model and continuing its training on domain-specific or task-specific data to specialize it for a particular use case. Fine-tuning does not retrain the FM from scratch (which would be costly and time-consuming). Instead, it improves model accuracy, relevance, and contextual adaptation for the intended application (e.g., legal, healthcare, customer support).
From AWS Docs:
"With Amazon Bedrock, you can fine-tune foundation models on your own data to specialize them for your unique use cases."
"Fine-tuning a foundation model adapts it to a specific task by training on smaller sets of labeled data relevant to the problem domain." Reference:
AWS Documentation - Fine-tuning foundation models in Amazon Bedrock
NEW QUESTION # 188
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: C
Explanation:
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.
Option D (Correct): "AWS Data Exchange": This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.
Option A: "AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.
Option B: "AWS Artifact" is incorrect as it provides access to AWS's compliance reports, not ISVs'.
Option C: "AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner References:
AWS Data Exchange Documentation: AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.
NEW QUESTION # 189
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