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NEW QUESTION # 21
A company is developing an editorial assistant application that uses generative AI. During the pilot phase, usage is low and application performance is not a concern. The company cannot predict application usage after the application is fully deployed and wants to minimize application costs.
Which solution will meet these requirements?
Answer: A
Explanation:
The correct answer is C because Amazon Bedrock with On-Demand Throughput is designed for variable or unpredictable workloads. It allows you to pay only for what you use, which is ideal for early-stage or pilot- phase generative AI applications.
From AWS documentation:
"On-Demand Throughput in Amazon Bedrock provides flexibility and cost-efficiency for unpredictable or low-volume generative AI applications. You are charged based on the number of input/output tokens processed, with no need to provision dedicated capacity." Explanation of other options:
A). Using GPU-powered EC2 instances requires managing infrastructure and is generally more expensive, especially for inconsistent usage.
B). Provisioned Throughput is best suited for predictable, high-volume production workloads where guaranteed throughput is needed.
D). SageMaker JumpStart is for training and deploying ML models, not for efficient inference of foundation models.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Pricing and Throughput Models
* AWS Generative AI Best Practices - Cost Optimization
* AWS ML Specialty Guide - Bedrock Deployment Models
NEW QUESTION # 22
A company wants to make a trained model available to production applications through an API endpoint for runtime queries.
Which ML lifecycle phase does this activity represent?
Answer: C
Explanation:
The verified answer is D. Model deployment and inference. The question describes a trained model being made available to production applications through an API endpoint. That is deployment, and the runtime use of the model to answer queries is inference. AWS SageMaker documentation states that after training, you can get predictions, or inferences, from trained machine learning models, and SageMaker provides model deployment options to support ML inference needs. This directly matches the phrase "available to production applications through an API endpoint for runtime queries." Data preparation is incorrect because that phase happens before training. It includes collecting, cleaning, transforming, labeling, and preparing datasets so the model can learn from them. The question already says the model is trained, so the workflow has moved beyond data preparation.
Model training and tuning is incorrect because training and tuning are where the model learns patterns from data and hyperparameters may be adjusted to improve performance. The question is not describing learning, optimization, or retraining. It describes exposing the trained model to applications.
Model evaluation and validation is incorrect because evaluation checks whether the model meets quality, accuracy, fairness, safety, or business criteria before or during production use. The scenario does not describe testing the model; it describes making the model callable by production applications.
In the ML lifecycle, deploying a model means placing it into an environment where applications can send requests and receive predictions. Inference is the process of using the deployed model to generate outputs from new inputs. Because the company is exposing the trained model through an API endpoint for runtime queries, the activity is model deployment and inference.
NEW QUESTION # 23
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.
Which business objective should the company use to evaluate the effect of the LLM chatbot?
Answer: B
NEW QUESTION # 24
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?
Answer: B
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 # 25
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
Answer: D
NEW QUESTION # 26
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