Pass the Amazon Web Services AWS Certified AI Practitioner AIF-C01 Questions and answers with ExamsMirror

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 Explanation:

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.

According to the AWS Certified AI Practitioner documentation, explainable AI (XAI) refers to methods and techniques that make the behavior and predictions of machine learning models more understandable and transparent to users and stakeholders. In financial use cases, especially when decisions such as credit limits are made, regulatory and ethical concerns demand transparency about how such decisions are reached.

Option B is correct because applying explainable AI techniques (such as SHAP, LIME, or Amazon SageMaker Clarify) allows organizations to provide customers with clear insights into which data points or factors contributed to the model's decision. This aligns with best practices for responsible AI as defined in the AWS documentation, which states:

"Explainable AI increases transparency and trust in machine learning applications by helping users and regulators understand the decision process behind model predictions."

(Reference: AWS AI/ML Best Practices – Explainable AI, AWS AI Practitioner Exam Guide)

Option A suggests switching to a rule-based system, which is not practical for complex problems addressed by generative AI and may reduce model performance.

Option C (just a UI) does not inherently provide transparency into the model’s reasoning, unless paired with explainability techniques.

Option D (accuracy over transparency) does not address the company’s requirement for transparency.

AWS Glue is a serverless data integration service that enables users to extract, transform, and load (ETL) data. For a company with minimal programming experience, Amazon Q Developer provides an AI-powered assistant that can generate code, explain AWS services, and guide users through tasks like creating AWS Glue jobs. This makes it an ideal tool to help the company use AWS Glue effectively.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Amazon Q Developer:

"Amazon Q Developer is an AI-powered assistant that helps developers by generating code, answering questions about AWS services, and providing step-by-step guidance for tasks such as building ETL pipelines with AWS Glue. It is designed to assist users with varying levels of expertise, including those with minimal programming experience."

(Source: AWS Documentation, Amazon Q Developer Overview)

Detailed Explanation:

Option A: Amazon Q Developer

This is the correct answer. Amazon Q Developer can assist the company by generating AWS Glue scripts, explaining Glue concepts, and providing guidance on setting up ETL jobs, which is particularly helpful for users with limited programming experience.

Option B: AWS Config

AWS Config is used for tracking and managing resource configurations and compliance, not for assisting with coding or using services like AWS Glue. This option is incorrect.

Option C: Amazon Personalize

Amazon Personalize is a machine learning service for building recommendation systems, not for assisting with data integration or AWS Glue. This option is irrelevant.

Option D: Amazon Comprehend

Amazon Comprehend is an NLP service for analyzing text, not for helping users write code or use AWS Glue. This option does not meet the requirements.

Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.

Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns": This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.

Option A: "Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.

Option B: "Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third-party model provider.

Option C: "Refining an existing third-party generative AI FM by fine-tuning the model with business-specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.

AWS AI Practitioner References:

Generative AI Security Scoping Matrix on AWS: AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.

The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining."

(Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models)

Detailed Explanation:

Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.

Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.

Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective.

Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM’s responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.

Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one-dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.

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.

Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.

Fine-Tuning Strategy:

To fine-tune a model, labeled data that pairs input prompts with the correct outputs (completions) is necessary.

This allows the model to learn the desired behavior or response style based on the provided examples.

Why Option A is Correct:

Proper Training Format: The prompt-completion pairs provide the necessary format for training the model to produce accurate outputs.

Customization: Ensures that the model is fine-tuned to the specific requirements of the company’s data and desired outputs.

Why Other Options are Incorrect:

B. Prepare a .txt file in .csv format: This does not align with the specific need for labeled data with prompts and completions.

C. Purchase Provisioned Throughput: Relates to read/write capacity in databases, not to model fine-tuning.

D. Train on journals and textbooks: Lacks the specific format and labeling required for fine-tuning.

The average response time is the correct metric for measuring the runtime efficiency of operating AI models.

Average Response Time:

Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.

A lower average response time indicates a more efficient model that can handle queries quickly.

Why Option C is Correct:

Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.

Performance Indicator: Helps identify potential bottlenecks and optimize model performance.

Why Other Options are Incorrect:

A. Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.

B. Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.

D. Number of training instances: Refers to data used during training, not operational efficiency.

Object detection is the correct strategy for automatically identifying and categorizing animals in photos.

Object Detection:

A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.

Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.

Why Option A is Correct:

Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.

Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.

Why Other Options are Incorrect:

B. Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.

C. Named entity recognition: Used in NLP to identify entities in text, not for image processing.

D. Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.