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問題 #280
Which metric measures the runtime efficiency of operating AI models?
答案：B
解題說明：
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.
問題 #281
A company has developed a neural network model to replace an existing decision tree model. The neural network model has a higher prediction accuracy compared to the decision tree model. However, the neural network model's decision process is not as explainable as the decision tree model's decision process.
Which tradeoff is the company making by adopting the neural network model?
答案：A
解題說明：
The verified answer is C. Higher performance for lower interpretability . The question states that the neural network model has higher prediction accuracy than the decision tree model, but its decision process is less explainable. In machine learning, this is a classic tradeoff between predictive performance and interpretability. AWS machine learning guidance discusses the tradeoff between performance and model interpretability and explains that interpretability becomes especially important when there is a high cost for incorrect predictions or when stakeholders need to understand decisions made by the model.
A decision tree is generally more interpretable because its decision path can often be traced through visible splits and rules. A neural network, especially a deeper model, often captures complex nonlinear relationships and can achieve higher prediction accuracy, but its internal decision process is usually harder to explain directly. AWS SageMaker Clarify documentation supports this distinction by explaining that model explainability tools help explain how machine learning models make predictions and help stakeholders understand model characteristics before deployment and debug predictions after deployment.
Option A is incorrect because lower interpretability does not produce higher compliance. In regulated environments, lower interpretability can create more governance and compliance challenges, not fewer.
Option B is incorrect because portability is not the issue described. The question does not discuss moving the model across environments or platforms. Option D is incorrect for the same reason: portability is unrelated to the stated facts.
The company is choosing the model with better predictive accuracy, which means higher model performance.
But it is accepting a weaker ability to explain how the model reaches its predictions. That is why the correct tradeoff is higher performance for lower interpretability .
問題 #282
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?
答案：D
解題說明：
The manufacturing company needs to create product descriptions in multiple languages, which requires automated language translation. Amazon Translate is a fully managed service that uses machine learning to provide high-quality translation between languages, making it the ideal solution for this task.
Exact Extract from AWS AI Documents:
From the Amazon Translate Developer Guide:
"Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. It can be used to automatically translate text, such as product descriptions, into multiple languages to reach a global audience." (Source: Amazon Translate Developer Guide, Introduction to Amazon Translate) Detailed Option A: Amazon TranslateThis is the correct answer. Amazon Translate automates the translation of text into multiple languages, directly addressing the company's need to create product descriptions in different languages.
Option B: Amazon TranscribeAmazon Transcribe converts speech to text, which is unrelated to translating text into multiple languages. This option is incorrect.
Option C: Amazon KendraAmazon Kendra is an intelligent search service that uses machine learning to provide answers from documents, not for translating text. This option is irrelevant.
Option D: Amazon PollyAmazon Polly is a text-to-speech service that generates spoken audio from text, not for translating text into other languages. This option does not meet the requirements.
Reference:
Amazon Translate Developer Guide: Introduction to Amazon Translate (https://docs.aws.amazon.com/translate/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Natural Language Processing Services AWS Documentation: Language Translation with Amazon Translate (https://aws.amazon.com/translate/)
問題 #283
Select the correct prompt engineering technique from the following list for each description. Select each prompt engineering technique one time or not at all. (Select THREE.)
* Chain-of-thought prompting
* Few-shot prompting
* Role-based prompting
* Single-shot prompting
* Zero-shot prompting
答案：
解題說明：
The verified selections are Few-shot prompting, Chain-of-thought prompting, and Zero-shot prompting. The first description matches few-shot prompting because AWS describes few-shot prompting as a technique that includes example outputs or demonstrations in the initial prompt so the model can understand the expected pattern before generating a response. The phrase "provide a small number of examples" is the key indicator.
A few-shot prompt gives the model limited examples of the desired task, format, or reasoning style, and the model uses those examples as context for the next output.
The second description matches chain-of-thought prompting. AWS describes chain-of-thought prompting as a technique that helps a model solve a problem by following a series of intermediate reasoning steps before reaching the final answer. The wording "break down the step-by-step process" directly points to chain-of- thought prompting. This method is commonly associated with reasoning, arithmetic, logic, planning, and multi-step problem solving because the prompt encourages the model to work through intermediate steps rather than immediately outputting a final answer.
The third description matches zero-shot prompting because AWS describes zero-shot prompting as asking the model to perform a task without providing examples in the prompt. The model relies only on the instruction and its pre-trained knowledge. The phrase "without providing examples" is the decisive clue.
Role-based prompting is not used here because none of the descriptions asks the model to act as a specific persona, job role, or domain expert, such as "Act as a financial analyst" or "You are a security engineer." Single-shot prompting is also not used because the first description says a "small number of examples," which indicates few-shot prompting, not exactly one example. Therefore, the three correct hotspot mappings are Few-shot prompting, Chain-of-thought prompting, and Zero-shot prompting.
問題 #284
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
答案：D
解題說明：
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner Reference:
Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
問題 #285
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