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Amazon AIF-C01 Dumps

Get the latest Amazon AIF-C01 exam dumps from Dumps4Solution. Pass your AWS AI Specialty exam with top-tier, updated materials, practice questions, and expert support.

Exam Code AIF-C01
Exam Name AWS Certified AI Practitioner
Update Date 14 Sep, 2024
Total Questions 65 Questions Answers With Explanation
$99

The AWS Certified AI Practitioner (AIF-C01) is a new certification that aims at testing and verifying the practical knowledge of AI, ML, and generative AI concepts, particularly regarding AWS. .. Launch Date: As of this writing, August 13, 2024, is when registration for the AIF-C01 exam opened. The exam is still in beta testing. Format: You have 120 minutes to finish the 85 multiple-choice questions in the test. Cost: $75 USD (or 10,000 JPY) is the exam fee.

Target Audience: While it is targeted for people who work with AI/ML with AWS but do not develop AI/ML solutions. These candidates are usually business analysts, marketing personnel, and IT support personnel.

Exam Topics

The AIF-C01 exam covers a wide range of topics, including:

  • Fundamental concepts and terminologies of AI, ML, and generative AI
  • Use cases for AI, ML, and generative AI
  • Design considerations for foundation models
  • Model training and fine-tuning
  • Prompt engineering and evaluation criteria for foundation models
  • Responsible AI practices and security compliance for AI systems.

Exam Format

The test will contain different types of questions including ordering, matching, multiple choice, multiple response and case study items. Candidates will be given a scaled score in between one thousand to one hundred, with the passing score of seven hundred set.

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Question # 1

A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources. Which AI learning strategy provides this self-improvement capability? 

A. Supervised learning with a manually curated dataset of good responses and bad responses 
B. Reinforcement learning with rewards for positive customer feedback 
C. Unsupervised learning to find clusters of similar customer inquiries 
D. Supervised learning with a continuously updated FAQ database 

Question # 2

How can companies use large language models (LLMs) securely on Amazon Bedrock?

 A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
 B. Enable AWS Audit Manager for automatic model evaluation jobs. 
C. Enable Amazon Bedrock automatic model evaluation jobs.
 D. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias. 

Question # 3

A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals. Which model evaluation strategy meets these requirements? 

A. Bilingual Evaluation Understudy (BLEU)
 B. Root mean squared error (RMSE)
 C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) 
D. F1 score 

Question # 4

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock? 

A. Purchase Provisioned Throughput for the custom model. 
B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
 C. Register the model with the Amazon SageMaker Model Registry.
 D. Grant access to the custom model in Amazon Bedrock. 

Question # 5

An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images. Which type of FM should the AI practitioner use to power the search application? 

A. Multi-modal embedding model 
B. Text embedding model 
C. Multi-modal generation model
 D. Image generation model 

Question # 6

Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

 A. Helps decrease the model's complexity 
B. Improves model performance over time 
C. Decreases the training time requirement 
D. Optimizes model inference time 

Question # 7

Which metric measures the runtime efficiency of operating AI models? 

A. Customer satisfaction score (CSAT)
 B. Training time for each epoch 
C. Average response time 
D. Number of training instances

Question # 8

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?

 A. Generative pre-trained transformers (GPT) 
B. Residual neural network 
C. Support vector machine 
D. WaveNet

Question # 9

Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

 A. Calculate the total cost of resources used by the model. 
B. Measure the model's accuracy against a predefined benchmark dataset. 
C. Count the number of layers in the neural network. 
D. Assess the color accuracy of images processed by the model.

Question # 10

A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals. Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

 A. User-generated content 
B. Moderation logs 
C. Content moderation guidelines 
D. Benchmark datasets