AIF-C01 Exam Dumps - AWS Certified AI Practitioner Exam(AI1-C01)

Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.

Data Augmentation for Imbalanced Classes:

Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.

Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.

Why Option A is Correct:

Balances Data Distribution:Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.

Improves Model Fairness:Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.

Why Other Options are Incorrect:

B. Model monitoring for class distribution:Helps identify bias but does not actively correct it.

C. Retrieval Augmented Generation (RAG):Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.

D. Watermark detection for images:Detects watermarks in images, not a technique for addressing bias.

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.

To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.

Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets":This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.

Option A:"Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.

Option C:"Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.

Option D:"Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.

AWS AI Practitioner References:

Model Evaluation Techniques on AWS:AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences

Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.

Few-shot Learning for Intent Detection:

Few-shot learning aims to enable the model to learn from a small number of examples. For intent detection, the model needs to understand the relationship between user messages and the intended action or meaning.

Providing examples of user messages and the correct user intents allows the model to learn patterns in the phrasing or language that corresponds to each intent.

Why Option C is Correct:

User Messages and Intents:These examples directly teach the model how to map a user’s input to the appropriate intent, which is the goal of intent detection in chatbots.

Improves Accuracy:By using few-shot learning with these examples, the model can generalize better from limited data, improving intent detection.

Why Other Options are Incorrect:

A. Pairs of chatbot responses and correct user intents:Incorrect because it does not focus on user input but rather on outputs.

B. Pairs of user messages and correct chatbot responses:This would be useful for response generation, not intent detection.

D. Pairs of user intents and correct chatbot responses:Again, this is not aligned with detecting intents but with generating responses.

Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.

Option A (Correct): "Real-time inference":This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.

Option B:"Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.

Option C:"Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.

Option D:"Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.

AWS AI Practitioner References:

Amazon SageMaker Inference Options:AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.