RELIABLE AIF-C01 EXAM QUESTION - EXAM QUESTIONS AIF-C01 VCE

Reliable AIF-C01 Exam Question - Exam Questions AIF-C01 Vce

Reliable AIF-C01 Exam Question - Exam Questions AIF-C01 Vce

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Amazon AWS Certified AI Practitioner Sample Questions (Q93-Q98):

NEW QUESTION # 93
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?

  • A. AWS Artifact
  • B. AWS Trusted Advisor
  • C. AWS Audit Manager
  • D. AWS Data Exchange

Answer: A


NEW QUESTION # 94
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

  • A. Prescriptive ML models
  • B. Topic modeling
  • C. Clustering models
  • D. BERT-based models

Answer: D

Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.


NEW QUESTION # 95
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?

  • A. Use AWS A] Service Cards for transparency and understanding models.
  • B. Create model training scripts. Commit the model training scripts to a Git repository.
  • C. Create documents with the relevant information. Store the documents in Amazon S3.
  • D. Create Amazon SageMaker Model Cards with Intended uses and training and inference details.

Answer: D

Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Explanation:
Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
References:
Amazon SageMaker Developer Guide: SageMaker Model Cards (http://docs.aws.amazon.com/sagemaker
/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (http://aws.amazon.com/sagemaker/)


NEW QUESTION # 96
An AI practitioner is developing a prompt for an Amazon Titan model. The model is hosted on Amazon Bedrock. The AI practitioner is using the model to solve numerical reasoning challenges. The AI practitioner adds the following phrase to the end of the prompt: "Ask the model to show its work by explaining its reasoning step by step." Which prompt engineering technique is the AI practitioner using?

  • A. Prompt templating
  • B. Few-shot prompting
  • C. Chain-of-thought prompting
  • D. Prompt injection

Answer: C

Explanation:
Chain-of-thought prompting is a prompt engineering technique where you instruct the model to explain its reasoning step by step, which is particularly useful for tasks involving logic, math, or reasoning.
A is correct: Asking the model to "explain its reasoning step by step" directly invokes chain-of-thought prompting, as documented in AWS and generative AI literature.
B is unrelated (prompt injection is a security concern).
C (few-shot) provides examples, but doesn't specifically require step-by-step reasoning.
D (templating) is about structuring the prompt format.
"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks." (Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)
"Chain-of-thought prompting elicits step-by-step explanations from LLMs, which improves performance on complex reasoning tasks." (Reference: Amazon Bedrock Prompt Engineering Guide, AWS Certified AI Practitioner Study Guide)


NEW QUESTION # 97
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem

Answer:

Explanation:

Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (http://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-mlconcepts.html) AWS Well-Architected Framework: Machine Learning Lens (http://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)


NEW QUESTION # 98
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