Latest AIF-C01 Test Question - AIF-C01 Examcollection Dumps
Latest AIF-C01 Test Question - AIF-C01 Examcollection Dumps
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Tags: Latest AIF-C01 Test Question, AIF-C01 Examcollection Dumps, AIF-C01 Dumps PDF, AIF-C01 New Dumps Files, AIF-C01 New Study Questions
The AWS Certified AI Practitioner (AIF-C01) certification is a requirement if you want to succeed in the Amazon industry quickly. But after deciding to take the AIF-C01 exam, the next challenge you face is the inability to find genuine AIF-C01 Questions for quick preparation. People who don't study with AIF-C01 real dumps fail the test and lose their precious resources.
Amazon AIF-C01 Exam Syllabus Topics:
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>> Latest AIF-C01 Test Question <<
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This format of our AIF-C01 product is easiest to use due to its compatibility with web-browsers. This handy feature makes it your go-to online platform to evaluate your preparation. Conceptual and tough AIF-C01 questions will prompt on your screen which will test your true concepts. Amazon Certification Exams Questions taken from past papers will also be given to give you a brief idea of the actual difficulty level of the AWS Certified AI Practitioner (AIF-C01) exam. Its large question bank prepares you to ace your exam with ease and it will also help you to pinpoint your mistakes and weaknesses and work on them.
Amazon AWS Certified AI Practitioner Sample Questions (Q29-Q34):
NEW QUESTION # 29
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?
- A. Amazon SageMaker Clarify
- B. Amazon SageMaker Model Cards
- C. Amazon SageMaker Data Wrangler
- D. Amazon SageMaker Feature Store
Answer: D
NEW QUESTION # 30
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?
- A. R2 score
- B. Confusion matrix
- C. Mean squared error (MSE)
- D. Correlation matrix
Answer: B
NEW QUESTION # 31
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
- A. AWS PrivateLink
- B. Amazon Macie
- C. Internet gateway
- D. Amazon CloudFront
Answer: A
Explanation:
AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.
* Option A (Correct): "AWS PrivateLink": This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.
* Option B: "Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.
* Option C: "Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.
* Option D: "Internet gateway" is incorrect as it enables internet access, which violates the VPC's no- internet-traffic policy.
AWS AI Practitioner References:
* AWS PrivateLink Documentation: AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.
NEW QUESTION # 32
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
- A. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
- B. Evaluate the models by using a human workforce and custom prompt datasets.
- C. Use public model leaderboards to identify the model.
- D. Evaluate the models by using built-in prompt datasets.
Answer: B
Explanation:
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
* Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
* Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
* Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
* Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner References:
* Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences
NEW QUESTION # 33
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. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
- B. F1 score
- C. Bilingual Evaluation Understudy (BLEU)
- D. Root mean squared error (RMSE)
Answer: C
Explanation:
BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.
* Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.
* Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.
* Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.
* Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.
AWS AI Practitioner References:
* Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.
NEW QUESTION # 34
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