A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?
A. Fine-tune the model regularly.
B. Train the model by using context data.
C. Pre-train and benchmark the model by using context data.
D. Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.
Explanation: RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date.
A company wants to use language models to create an application for inference on edge
devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?
A. Deploy optimized small language models (SLMs) on edge devices.
B. Deploy optimized large language models (LLMs) on edge devices.
C. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
A retail store wants to predict the demand for a specific product for the next few weeks by
using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement?
A. Text data
B. Image data
C. Time series data
D. Binary data
Explanation: 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.
A company has thousands of customer support interactions per day and wants to analyze
these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
A. Amazon Lex
B. Amazon Comprehend
C. Amazon Transcribe
D. Amazon Translate
Explanation: Amazon Comprehend is the correct service to analyze customer support interactions and identify frequently asked questions and insights.
An AI company periodically evaluates its systems and processes with the help of
independent software vendors (ISVs). The company needs to receive email message
notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
A. AWS Audit Manager
B. AWS Artifact
C. AWS Trusted Advisor
D. AWS Data Exchange
A company is using Amazon SageMaker Studio notebooks to build and train ML models.
The company stores the data in an Amazon S3 bucket. The company needs to manage the
flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?
A. Use Amazon Inspector to monitor SageMaker Studio.
B. Use Amazon Macie to monitor SageMaker Studio.
C. Configure SageMaker to use a VPC with an S3 endpoint.
D. Configure SageMaker to use S3 Glacier Deep Archive.
A loan company is building a generative AI-based solution to offer new applicants
discounts based on specific business criteria. The company wants to build and use an AI
model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)
A. Detect imbalances or disparities in the data.
B. Ensure that the model runs frequently.
C. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
D. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
E. Ensure that the model's inference time is within the accepted limits.
Which metric measures the runtime efficiency of operating AI models?
A. Customer satisfaction score (CSAT)
B. Training time for each epoch
C. Average response time
D. Number of training instances
An accounting firm wants to implement a large language model (LLM) to automate
document processing. The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)
A. Include fairness metrics for model evaluation.
B. Adjust the temperature parameter of the model.
C. Modify the training data to mitigate bias.
D. Avoid overfitting on the training data.
E. Apply prompt engineering techniques.
A company is building a customer service chatbot. The company wants the chatbot to
improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
A. Supervised learning with a manually curated dataset of good responses and bad responses
B. Reinforcement learning with rewards for positive customer feedback
C. Unsupervised learning to find clusters of similar customer inquiries
D. Supervised learning with a continuously updated FAQ database
A company wants to create an application by using Amazon Bedrock. The company has a
limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
A. On-Demand
B. Model customization
C. Provisioned Throughput
D. Spot Instance
What are tokens in the context of generative AI models?
A. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
B. Tokens are the mathematical representations of words or concepts used in generative AI models.
C. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
D. Tokens are the specific prompts or instructions given to a generative AI model to generate output.
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