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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 2
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 3
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 4
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 5
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

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Amazon AWS Certified AI Practitioner Sample Questions (Q37-Q42):

NEW QUESTION # 37
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?

Answer: D

Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 38
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.
Which solution meets these requirements?

Answer: D


NEW QUESTION # 39
An ecommerce company is developing a generative Al solution to create personalized product recommendations for its application users. The company wants to track how effectively the Al solution increases product sales and user engagement in the application.
Select the correct business metric from the following list for each business goal. Each business metric should be selected one time. (Select THREE.) Average order value (AOV) Click-through rate (CTR) Retention rate

Answer:

Explanation:


NEW QUESTION # 40
A company has created a custom model by fine-tuning an existing large language model (LLM) from Amazon Bedrock. The company wants to deploy the model to production and use the model to handle a steady rate of requests each minute.
Which solution meets these requirements MOST cost-effectively?

Answer: A

Explanation:
The correct answer is D - Purchase Provisioned Throughput on Amazon Bedrock, which provides guaranteed and predictable model capacity for workloads with consistent or steady request volume. According to AWS Bedrock documentation, Provisioned Throughput is specifically designed for production applications that require reliable, consistent inference performance at a controlled cost. It allows customers to reserve a fixed number of model inference units (MIUs), ensuring low latency and cost savings compared to on-demand pricing when traffic is steady. On-demand throughput (option B) is ideal for unpredictable or sporadic usage, but it becomes more expensive for stable traffic patterns because it charges per token with no discount for steady volume. Hosting the model on EC2 (option A) or Lambda (option C) increases operational overhead, requires model containerization, scaling management, and may not support LLM-level GPU performance efficiently. Bedrock Provisioned Throughput eliminates infrastructure management and provides the most cost-effective solution for stable, predictable workloads.
Referenced AWS Documentation:
* Amazon Bedrock Developer Guide - Provisioned Throughput
* AWS ML Specialty Study Guide - Cost Optimization for Generative AI


NEW QUESTION # 41
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.)
* Computer vision
* Natural language processing (NLP)
* Reinforcement learning
* Time series forecasting

Answer:

Explanation:

Explanation:

Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP)
* NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision
* Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting
* Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).


NEW QUESTION # 42
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