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Amazon AWS Certified AI Practitioner Sample Questions (Q253-Q258):

NEW QUESTION # 253
A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows 90% recall on training data and 40% recall on unseen testing data.
Which conclusion can the company draw from these results?

Answer: D

Explanation:
The ML model shows 90% recall on training data but only 40% recall on unseen testing data, indicating a significant performance drop. This discrepancy suggests the model has learned the training data too well, including noise and specific patterns that do not generalize to new data, which is a classic sign of overfitting.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting occurs when a model performs well on training data but poorly on unseen test data, as it has learned patterns specific to the training set, including noise, that do not generalize. A large gap between training and testing performance metrics, such as recall, is a common indicator of overfitting." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Option A: The model is overfitting on the training data.This is the correct answer. The significant drop in recall from 90% (training) to 40% (testing) indicates the model is overfitting, as it performs well on training data but fails to generalize to unseen data.
Option B: The model is underfitting on the training data.Underfitting occurs when the model performs poorly on both training and testing data due to insufficient learning. With 90% recall on training data, the model is not underfitting.
Option C: The model has insufficient training data.Insufficient training data could lead to poor performance, but the high recall on trainingdata (90%) suggests the model has learned the training data well, pointing to overfitting rather than a lack of data.
Option D: The model has insufficient testing data.Insufficient testing data might lead to unreliable test metrics, but it does not explain the large performance gap between training and testing, which is more indicative of overfitting.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting and Underfitting (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 254
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?

Answer: A

Explanation:
Transfer learning is the correct strategy for adapting pre-trained models for new, related tasks without creating models from scratch.
* Transfer Learning:
* Involves taking a pre-trained model and fine-tuning it on a new dataset for a related task.
* This approach is efficient because it leverages existing knowledge from a model trained on a large dataset, requiring less data and computational resources than training a new model from scratch.
* Why Option B is Correct:
* Adaptation of Pre-trained Models: Allows for adapting existing models to new tasks, which aligns with the company's goal of not starting from scratch.
* Efficiency and Speed: Speeds up the model development process by building on the knowledge of pre-trained models.
* Why Other Options are Incorrect:
* A. Increase the number of epochs: Does not address the strategy of reusing pre-trained models.
* C. Decrease the number of epochs: Similarly, does not apply to adapting pre-trained models.
* D. Use unsupervised learning: Does not involve using pre-trained models for new tasks.


NEW QUESTION # 255
A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.
Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times. (Select THREE.)
* Batch transform
* Real-time inference

Answer:

Explanation:
Use Case 1:
The company's chatbot needs predictions from the LLM to understand users ' intent with minimal latency.
# The answer: Real-time inference
Chatbots require low-latency, immediate responses to user input. Real-time inference is ideal for these interactive use cases.
Use Case 2:
A data processing job needs to query the LLM to process gigabytes of text files on weekends.
# The answer: Batch transform
Batch transform is designed for asynchronous, high-throughput jobs where latency is not critical. It's ideal for scheduled or large-scale processing like weekend batch jobs on large datasets.
Use Case 3:
The company's engineering team needs to create an API that can process small pieces of text content and provide low-latency predictions.
# The answer: Real-time inference
An API requiring fast response time for small content sizes is best served by real-time inference to meet latency requirements.


NEW QUESTION # 256
A media streaming platform wants to provide movie recommendations to users based on the users' account history.

Answer: C

Explanation:
* Amazon Personalize is a fully managed ML service for personalized recommendations (movies, products, music, etc.) based on user behavior and history.
* Polly converts text to lifelike speech.
* Comprehend performs NLP tasks like sentiment analysis.
* Transcribe is speech-to-text.
# Reference:
AWS Documentation - Amazon Personalize


NEW QUESTION # 257
How can companies use large language models (LLMs) securely on Amazon Bedrock?

Answer: C

Explanation:
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents.
* Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management.
* Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.
* Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.
* Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.
AWS AI Practitioner References:
* Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.


NEW QUESTION # 258
......

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