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Amazon AWS Certified AI Practitioner Sample Questions (Q187-Q192):

NEW QUESTION # 187
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
B. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
C. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

Answer: D

Explanation:
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.
Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.
Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.
Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.
Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.
AWS AI Practitioner Reference:
Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

NEW QUESTION # 188
A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.
Which AWS service meets these requirements?

A. Amazon CloudWatch
B. AWS CloudTrail
C. AWS Trusted Advisor
D. AWS Config

Answer: A

Explanation:
Amazon CloudWatch is designed for real-time monitoring of applications and infrastructure. It supports metrics and logs for ML model performance and resource utilization. According to the AWS Certified AI Practitioner Study Guide:
"Amazon CloudWatch is a monitoring service that provides data and actionable insights to monitor your ML workloads and applications in real time, ensuring performance and scalability."

NEW QUESTION # 189
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. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
C. F1 score
D. Root mean squared error (RMSE)

Answer: A

NEW QUESTION # 190
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.

Answer:

Explanation:

Explanation:
Step 1: Store meeting audio recordings in an Amazon S3 bucket.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Step 3: Summarize meeting text files by using Amazon Bedrock.
The company wants to create an application to summarize meeting audio recordings, which requires a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.
Exact Extract from AWS AI Documents:
From the Amazon Transcribe Developer Guide:
"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket." From the AWS Bedrock User Guide:
"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting transcripts." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User Guide, Text Generation and Summarization) Detailed Explanation:
* Step 1: Store meeting audio recordings in an Amazon S3 bucket.Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.
* Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.
* Step 3: Summarize meeting text files by using Amazon Bedrock.Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application's requirements.
Unused Options Analysis:
* Convert meeting audio recordings to meeting text files by using Amazon Polly.Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.
* Summarize meeting text files by using Amazon Lex.Amazon Lex is for building conversational interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for summarization, so this option is not used.
Hotspot Selection Analysis:
The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps-storing in S3, converting with Transcribe, and summarizing with Bedrock-form a complete and logical workflow for the application.
References:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com
/transcribe/latest/dg/what-is.html)
AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest
/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest
/userguide/Welcome.html)

NEW QUESTION # 191
Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?

A. Overcoming challenges to drive business transformation and growth
B. Ensuring alignment with business standards, revenue goals, and stakeholder expectations
C. Expanding initiatives across business units to create long-term business value
D. Developing policies and guidelines for data, transparency, responsible AI, and compliance

Answer: D

Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
Reference:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)

NEW QUESTION # 192
......

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Amazon AWS Certified AI Practitioner Sample Questions (Q187-Q192):

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