Menu
Pass Guaranteed Quiz 2025 Amazon Useful Exam AIF-C01 Labs
Now you do not need to worry about the relevancy and top standard of PDFBraindumps AWS Certified AI Practitioner (AIF-C01) exam questions. These Amazon AIF-C01 dumps are designed and verified by qualified AWS Certified AI Practitioner (AIF-C01) exam trainers. Now you can trust PDFBraindumps AWS Certified AI Practitioner (AIF-C01) practice questions and start preparation without wasting further time.
Amazon AIF-C01 Exam Syllabus Topics:
Topic
Details
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
Pass Guaranteed Amazon - AIF-C01 - Efficient Exam AWS Certified AI Practitioner Labs
The PDFBraindumps team regularly revises the AWS Certified AI Practitioner (AIF-C01) PDF version to add new questions and update Amazonmation, so candidates are always up-to-date. We provide candidates with comprehensive AWS Certified AI Practitioner (AIF-C01) exam questions with up to 1 year of free updates. If you are doubtful, feel free to download a free demo of PDFBraindumps AWS Certified AI Practitioner (AIF-C01) PDF dumps, desktop practice exam software, and web-based AWS Certified AI Practitioner (AIF-C01) practice exam. Don't wait. Purchase AWS Certified AI Practitioner (AIF-C01) exam dumps at an affordable price and start preparing for the updated Amazon AIF-C01 certification exam today.
Amazon AWS Certified AI Practitioner Sample Questions (Q110-Q115):
NEW QUESTION # 110
Which AWS feature records details about ML instance data for governance and reporting?
Answer: D
Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
NEW QUESTION # 111
A company is building a contact center application and wants to gain insights from customer conversations.
The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
Answer: B
Explanation:
Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.
* Amazon Transcribe:
* It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.
* After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.
* Why Option B is Correct:
* Conversion to Text: Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.
* Enables Further Analysis: Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.
* Why Other Options are Incorrect:
* A. Amazon Lex: Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.
* C. Amazon SageMaker Model Monitor: Monitors ML models for bias and data drift, not for audio analysis.
* D. Amazon Comprehend: Can analyze text but cannot transcribe audio; it would be used after transcription is completed.
NEW QUESTION # 112
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?
Answer: A
Explanation:
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.
* Option B (Correct): "Enable invocation logging in Amazon Bedrock": This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
* Option A: "Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.
* Option C: "Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.
* Option D: "Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.
AWS AI Practitioner References:
* Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.
NEW QUESTION # 113
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
Answer: B
NEW QUESTION # 114
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Answer: D
Explanation:
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner Reference:
Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
NEW QUESTION # 115
......
It is because of our high quality Amazon AIF-C01 preparation software, PDF files and other relevant products, we have gathered thousands of customers who have successfully passed the Amazon AIF-C01 in one go. You can also attain the same success rate by using our high standard AIF-C01 Preparation products. Thousands of satisfied customers can't be wrong. You must try our products to believe this fact.
Dump AIF-C01 Check: https://www.pdfbraindumps.com/AIF-C01_valid-braindumps.html