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NEW QUESTION # 419
A company uses an Amazon Bedrock foundation model (FM) to summarize documents for an internal use case. The company trained a custom model in Amazon Bedrock to improve the quality of the model's summarizations. The company needs a solution to use the customized model on Amazon Bedrock.
Which solution will meet this requirement?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
When a foundation model is customized directly in Amazon Bedrock, the correct way to use the customized model for inference is to purchase Provisioned Throughput.
Provisioned Throughput:
* Enables consistent performance and predictable latency
* Allows production use of customized Bedrock models
* Is the required deployment mechanism for custom FMs in Bedrock
Why the other options are incorrect:
* SageMaker endpoints (B) are not used for Bedrock-native custom models.
* Model Registry (C) applies to SageMaker models.
* Approval status (D) does not deploy or enable usage.
AWS AI document references:
* Amazon Bedrock Custom Model Deployment
* Provisioned Throughput for Foundation Models
* Using Customized Models in Amazon Bedrock
NEW QUESTION # 420
A company is building a generative AI (GenAI) application. The company wants to implement mechanisms to monitor and direct AI system behavior.
Which responsible AI dimension is the company applying?
Answer: A
Explanation:
The verified answer is C. Controllability. AWS defines responsible AI through several dimensions, including fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. The exact wording in the question is the clue: the company wants mechanisms to monitor and direct AI system behavior. AWS describes controllability as having mechanisms to monitor and steer AI system behavior. That maps directly to the requirement.
Fairness is incorrect because fairness focuses on considering impacts on different groups of stakeholders. It is about avoiding unjust outcomes, biased treatment, or disproportionate harm across populations. The question does not describe demographic representation, biased outputs, or unequal model behavior.
Explainability is incorrect because explainability focuses on understanding and evaluating system outputs. It helps stakeholders understand why the model generated a result or how model behavior can be interpreted. The question is not asking about understanding a decision; it is asking about controlling and directing system behavior.
Safety is incorrect because safety focuses on reducing harmful system output and misuse. Safety is related, but the wording "monitor and direct AI system behavior" is specifically the AWS definition of controllability, not safety. Safety might include preventing toxic, harmful, or dangerous outputs, but controllability is the broader dimension about steering the system according to intended behavior.
Therefore, the responsible AI dimension being applied is controllability, because the company wants active mechanisms to guide, monitor, and steer how the generative AI application behaves in operation.
NEW QUESTION # 421
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?
Answer: C
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.
NEW QUESTION # 422
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?
Answer: D
Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
A . Measurement bias: Involves errors in data collection or measurement, not sampling.
C . Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.
D . Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.
NEW QUESTION # 423
Which phase of the ML lifecycle determines compliance and regulatory requirements?
Answer: B
Explanation:
The business goal identification phase of the ML lifecycle involves defining the objectives of the project and understanding the requirements, including compliance and regulatory considerations. This phase ensures the ML solution aligns with legal and organizational standards before proceeding to technical stages like data collection or model training.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The business goal identification phase involves defining the problem to be solved, identifying success metrics, and determining compliance and regulatory requirements to ensure the ML solution adheres to legal and organizational standards." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
* Option A: Feature engineeringFeature engineering involves creating or selecting features for model training, which occurs after compliance requirements are identified. It does not address regulatory concerns.
* Option B: Model trainingModel training focuses on building the ML model using data, not on determining compliance or regulatory requirements.
* Option C: Data collectionData collection involves gathering data for training, but compliance and regulatory requirements (e.g., data privacy laws) are defined earlier in the business goal identification phase.
* Option D: Business goal identificationThis is the correct answer. This phase ensures that compliance and regulatory requirements are considered at the outset, shaping the entire ML project.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: ML Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg
/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
NEW QUESTION # 424
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