AIF-C01 Exam Dumps - AWS Certified AI Practitioner Exam

Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.

Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.

Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.

Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.

Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.

AWS AI Practitioner References:

Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model’s parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks."

(Source: AWS Bedrock User Guide, Model Customization)

Detailed Explanation:

Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.

Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.

Option C: Model quantizationModel quantization reduces the precision of a model’s weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.

Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.

Artificial Intelligence (AI) is the broad field focused on simulating human problem-solving and cognitive abilities, including reasoning, perception, and decision-making.

(Reference: AWS Certified AI Practitioner Official Study Guide)

Machine Learning (ML) is a subset of AI that uses data-driven algorithms to identify patterns and make predictions without explicit programming for each specific task.

(Reference: AWS Machine Learning Overview)

Deep learning is a subset of ML that uses neural networks with many layers (deep neural networks) to process complex data and extract high-level features.

(Reference: AWS Deep Learning on AWS)

Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.

Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.

Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.

Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.

Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.

AWS AI Practitioner References:

Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.

Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into manageable parts for processing.

Option A (Correct): "Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units": This is the correct definition of tokens in the context of generative AI models.

Option B: "Mathematical representations of words" describes embeddings, not tokens.

Option C: "Pre-trained weights of a model" refers to the parameters of a model, not tokens.

Option D: "Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.

AWS AI Practitioner References:

Understanding Tokens in NLP: AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.

Comprehensive and Detailed Explanation from AWS AI Documents:

Classification requires predefined labels (categories). The company explicitly does not want to define categories.

Regression (linear or logistic) predicts numerical values or probabilities, not groups.

Clustering is an unsupervised learning technique that groups similar data points together based on their features without needing labeled categories.

AWS defines clustering as:

“Clustering is an unsupervised machine learning algorithm that automatically groups data points into clusters based on their similarities.”

This makes Clustering the correct choice for segmenting customers into groups without predefined labels.

📖 Reference:

AWS ML Glossary – Clustering

The business objective to evaluate the effect of an LLM chatbot aimed at reducing the actions required by call center employees should be average call duration.

Average Call Duration:

This metric measures the time taken to handle a customer call or query. A successful LLM chatbot should reduce the call duration by efficiently providing answers, minimizing the need for human intervention.

By decreasing the average call duration, the company can improve call center efficiency, reduce costs, and enhance the user experience.

Why Option B is Correct:

Direct Impact: The objective aligns directly with the goal of reducing the number of actions call center employees must take.

Operational Efficiency: Reducing call duration is a clear indicator of the chatbot's effectiveness in assisting customers without human help.

Why Other Options are Incorrect:

A. Website engagement rate: Is unrelated to call center operations.

C. Corporate social responsibility: Does not relate to call center efficiency.

D. Regulatory compliance: Is important but does not measure the effectiveness of a chatbot in reducing employee actions.