Most Recent Amazon AIF-C01 Exam Dumps

Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.

Option D (Correct): 'Building and training a generative AI model from scratch by using specific data that a customer owns': This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.

Option A: 'Using a third-party enterprise application that has embedded generative AI features' is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.

Option B: 'Building an application using an existing third-party generative AI foundation model (FM)' is incorrect because security responsibilities are shared with the third-party model provider.

Option C: 'Refining an existing third-party generative AI FM by fine-tuning the model with business-specific data' is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.

AWS AI Practitioner Reference:

Generative AI Security Scoping Matrix on AWS: AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.

Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one-dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.

The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.

Option D (Correct): 'Amazon EC2 Trn series': This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.

Option A: 'Amazon EC2 C series' is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.

Option B: 'Amazon EC2 G series' (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.

Option C: 'Amazon EC2 P series' is designed for ML training but does not offer the same level of energy efficiency as the Trn series.

AWS AI Practitioner Reference:

AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.

A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.

Option C (Correct): 'Develop a summarization chatbot': This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.

Option A: 'Build an automatic named entity recognition system' is incorrect because it focuses on identifying specific entities, not summarizing documents.

Option B: 'Create a recommendation engine' is incorrect as it is used to suggest products or content, not summarize text.

Option D: 'Develop a multi-language translation system' is incorrect because translation is unrelated to summarizing text.

AWS AI Practitioner Reference:

Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

The average response time is the correct metric for measuring the runtime efficiency of operating AI models.

Average Response Time:

Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.

A lower average response time indicates a more efficient model that can handle queries quickly.

Why Option C is Correct:

Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.

Performance Indicator: Helps identify potential bottlenecks and optimize model performance.

Why Other Options are Incorrect:

A . Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.

B . Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.

D . Number of training instances: Refers to data used during training, not operational efficiency.