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Amazon AWS Certified AI Practitioner Sample Questions (Q134-Q139):

NEW QUESTION # 134
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

A. Create an Amazon Bedrock knowledge base
B. Choose a lower temperature value
C. Enable model invocation logging
D. Use a different FM

Answer: A

Explanation:
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
* Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
* Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.
* Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
* Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner References:
* Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.

NEW QUESTION # 135
A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication. Which solution meets these requirements?

A. Create a classification model that categorizes medications into different groups by using Amazon SageMaker.
B. Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.
C. Create medication review summaries by using Amazon Rekognition.
D. Create medication review summaries by using Amazon Bedrock large language models (LLMs).

Answer: D

Explanation:
Amazon Bedrock provides large language models (LLMs) that are optimized for natural language understanding and text summarization tasks, making it the best choice for creating concise summaries of user reviews. Time-series forecasting, classification, and image analysis (Rekognition) are not suitable for summarizing textual data. References: AWS Bedrock Documentation.

NEW QUESTION # 136
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?

A. Asynchronous inference
B. Batch transform
C. Real-time inference
D. Serverless inference

Answer: C

Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

NEW QUESTION # 137
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. 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.
B. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
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 Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.

Answer: B

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 References:
* 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 # 138
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?

A. Reinforcement learning.
B. Active learning.
C. Supervised learning.
D. Unsupervised learning.

Answer: C

Explanation:
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.

NEW QUESTION # 139
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AIF-C01 Valid Test Fee | Valid AIF-C01 Test Cost

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Amazon AWS Certified AI Practitioner Sample Questions (Q134-Q139):

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