AIF-C01 Exam Dumps - Amazon Web Services AWS Certified AI Practitioner Questions and Answers

Inference is the correct answer because it is the AI process that occurs when a deployed model analyzes new data (such as an image) to make predictions or identify objects.

Inference:

In the context of machine learning, inference is the process of using a trained model to make predictions on new, unseen data.

When the deep learning model is deployed to production and receives a new image for analysis, it uses the learned patterns from the training phase to identify objects in the image. This is known as inference.

Why Option B is Correct:

Inference Process: Involves applying the trained model to real-world data (the new image) to identify objects.

Deployment Context: The model has already been trained, and the deployment to production indicates it is being used for inference.

Why Other Options are Incorrect:

A. Training: Refers to the process of teaching the model using historical data, not making predictions on new data.

C. Model deployment: Refers to the process of making a trained model available for use in production.

D. Bias correction: Is a process to adjust a model to minimize bias, not for analyzing new images.

Inference = applying a trained ML model to new, unseen data to make predictions, classifications, or generate outputs.

A is algorithm research, C refers to ensemble learning, D is data collection.

???? Reference:

AWS ML Glossary – Inference

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens: Would increase costs, not lower them.

D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

The correct answer is B because explainable AI (XAI) provides transparency into how models reach specific decisions. According to AWS documentation, techniques such as SHAP values (SHapley Additive exPlanations) or LIME can identify which input features (e.g., income, debt ratio, or credit history) most influenced a model’s prediction. This helps financial institutions comply with fairness and transparency requirements under regulatory frameworks like the Equal Credit Opportunity Act. AWS SageMaker Clarify is a built-in service that offers explainability reports and bias detection to enhance trust. Rule-based systems and UIs alone do not satisfy transparency standards, and accuracy improvements do not replace explainability. By implementing explainable AI, customers can understand and trust credit limit decisions, reducing bias concerns and ensuring compliance.

Referenced AWS AI/ML Documents and Study Guides:

Amazon SageMaker Clarify Documentation – Explainability and Feature Attribution

AWS Responsible AI Practices – Transparency and Accountability

AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Responsible AI:

"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment."

(Source: AWS Documentation, Responsible AI Governance)

Detailed Explanation:

Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.

Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.

Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.

Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.

Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.

Option C (Correct): "Amazon Q in Amazon QuickSight": This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.

Option A, B, and D: These options are incorrect:

A. Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.

B. Amazon Q Developer: This is not an existing AWS service or feature.

D. Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.

AWS AI Practitioner References:

Amazon QuickSight Q is designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.

Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.

Regression predicts continuous numerical values (e.g., stock prices, temperatures).

A is classification (genre selection).

B is clustering.

D is generative AI/computer vision.

???? Reference:

AWS ML Glossary – Regression

Comprehensive and Detailed Explanation (AWS AI documents):

AWS recommends using managed, purpose-built services to enforce safety, compliance, and responsible AI controls in generative AI applications in order to minimize operational complexity and maintenance effort.

Amazon Bedrock Guardrails are specifically designed to help customers:

Block or mask sensitive information, such as personally identifiable information (PII)

Detect and reduce hallucinations by enforcing grounding and response constraints

Apply content filters, topic restrictions, and safety policies consistently across generative AI applications

Configure safeguards without building or managing custom infrastructure

Because Guardrails are fully managed and integrated directly with Amazon Bedrock, they require minimal setup, no custom code for policy enforcement, and no infrastructure management, resulting in the least operational overhead.

Why the other options are less suitable:

A. AWS Lambda policy evaluator requires custom logic, testing, monitoring, and ongoing maintenance.

B. FM default policies alone are insufficient because they do not provide application-specific masking, hallucination detection, or configurable governance controls.

D. Custom EC2-based policy evaluators introduce the highest operational overhead due to server management, scaling, patching, and monitoring.

AWS AI Study Guide References:

Amazon Bedrock overview and safety features

Amazon Bedrock Guardrails for responsible generative AI

AWS best practices for building secure and governed generative AI applications

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.