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NEW QUESTION # 170
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning
Answer:
Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning
NEW QUESTION # 171
A research company needs to analyze legal documents. The documents are up to 1 million tokens long and include embedded high-resolution charts. The company also needs to ingest video summaries to generate compliance reports.
Which Amazon Nova model meets these requirements?
Answer: D
Explanation:
The verified answer is D. Amazon Nova Premier. The decisive requirement is the combination of a 1 million-token context window, multimodal document understanding, chart analysis, and video input. AWS documentation identifies Amazon Nova Premier as the most capable multimodal Amazon Nova understanding model. It supports text, image, and video inputs and generates text output. AWS also lists Amazon Nova Premier with a 1M context window, while Amazon Nova Pro and Amazon Nova Lite support 300k, and Amazon Nova Micro supports 128k and is text-only. Because the company's legal documents are up to 1 million tokens long, Nova Premier is the only listed option that satisfies the context-length requirement.
AWS documentation also states that Amazon Nova Premier has a supported context length of 1 million tokens, which AWS equates to 1 million tokens of text, 500 images, or 90 minutes of video. The same documentation explains that Nova Premier excels at question answering on long documents. This directly matches the research company's need to analyze very long legal documents and ingest video summaries for compliance reporting.
Amazon Nova Micro is incorrect because it is a text-only model and has a smaller context window. It cannot process embedded high-resolution charts or video inputs. Amazon Nova Lite is incorrect because it is multimodal, but its context window is 300k, which is below the 1 million-token requirement. Amazon Nova Pro is also incorrect for the same reason: it is highly capable and multimodal, but its context window is 300k rather than 1M. The requirement is not simply "multimodal"; it is long-context multimodal analysis at the 1 million-token scale. Therefore, the only model that fully meets the requirements is Amazon Nova Premier.
NEW QUESTION # 172
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?
Answer: D
Explanation:
Transfer learning is the correct strategy for adapting pre-trained models for new, related tasks without creating models from scratch.
* Transfer Learning:
* Involves taking a pre-trained model and fine-tuning it on a new dataset for a related task.
* This approach is efficient because it leverages existing knowledge from a model trained on a large dataset, requiring less data and computational resources than training a new model from scratch.
* Why Option B is Correct:
* Adaptation of Pre-trained Models: Allows for adapting existing models to new tasks, which aligns with the company's goal of not starting from scratch.
* Efficiency and Speed: Speeds up the model development process by building on the knowledge of pre-trained models.
* Why Other Options are Incorrect:
* A. Increase the number of epochs: Does not address the strategy of reusing pre-trained models.
* C. Decrease the number of epochs: Similarly, does not apply to adapting pre-trained models.
* D. Use unsupervised learning: Does not involve using pre-trained models for new tasks.
NEW QUESTION # 173
A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock.
Answer: A
Explanation:
Comprehensive and Detailed
With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).
Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.
Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.
Reference:
AWS Shared Responsibility Model
NEW QUESTION # 174
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?
Answer: C
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 # 175
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