HOTSPOT
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A company uses ML techniques to build applications.
Select the correct ML technique from the following list for each task. Select each ML technique one time.
Show Answer
Correct Answer: Analyze a text question to determine if the answer is correct. — Binary classification
Analyze ecological factors to determine the number of species in a certain area. — Regression
Analyze car attributes to determine the car model. — Multiclass classification
Explanation: Correct/incorrect is a binary outcome. Predicting the number of species is a numeric prediction, so regression. Predicting a car model chooses among multiple categories, so multiclass classification.
Question 195
An AI practitioner who has minimal ML knowledge wants to predict employee attrition without writing code.
Which Amazon SageMaker feature meets this requirement?
A. SageMaker Canvas
B. SageMaker Clarify
C. SageMaker Model Monitor
D. SageMaker Data Wrangler
Show Answer
Correct Answer: A
Explanation: SageMaker Canvas is the no-code machine learning service designed for users with little or no ML experience to build models and generate predictions through a visual interface. Clarify explains and detects bias in models, Model Monitor monitors deployed models, and Data Wrangler is for data preparation rather than no-code prediction.
Question 196
A company wants to learn about generative AI applications in an experimental environment.
Which solution will meet this requirement MOST cost-effectively?
A. Amazon Q Developer
B. Amazon SageMaker JumpStart
C. Amazon Bedrock PartyRock
D. Amazon Q Business
Show Answer
Correct Answer: C
Explanation: Amazon Bedrock PartyRock is a free, no-code playground built for experimenting with generative AI applications in an educational and experimental environment. It is the most cost-effective option for learning and prototyping. Amazon Q Developer is for developer productivity, Amazon Q Business is for enterprise knowledge assistants, and SageMaker JumpStart focuses on deploying and customizing ML and foundation models rather than a lightweight learning playground.
Question 197
A company wants to create a chatbot to answer employee questions about company policies. Company policies are updated frequently. The chatbot must reflect the changes in near real time. The company wants to choose a large language model (LLM).
Which solution meets these requirements?
A. Fine-tune an LLM on the company policy text by using Amazon SageMaker.
B. Select a foundation model (FM) from Amazon Bedrock to build an application.
C. Create a Retrieval Augmented Generation (RAG) workflow by using Amazon Bedrock Knowledge Bases.
D. Use Amazon Q Business to build a custom Q App.
Show Answer
Correct Answer: C
Explanation: A Retrieval Augmented Generation (RAG) solution with Amazon Bedrock Knowledge Bases retrieves the latest policy documents at query time, allowing the chatbot to reflect frequent policy updates without retraining or fine-tuning. Fine-tuning would quickly become outdated, selecting an FM alone does not address knowledge updates, and Amazon Q Business Q Apps are not the primary solution for building a custom policy chatbot with near real-time knowledge updates.
Question 198
A company wants to create an AI solution to generate images and descriptions for a product catalog. The company needs to select a foundation model (FM) for this solution.
The company must consider the output types of each FM.
Which FM characteristic is the company evaluating?
A. Latency
B. Model size
C. Model customization
D. Modality
Show Answer
Correct Answer: D
Explanation: The characteristic being evaluated is modality, which refers to the types of input and output a foundation model supports, such as text, images, audio, or video. Because the solution must generate both images and text descriptions, the company needs a model with the appropriate output modalities.
Question 199
A financial services company must ensure that its generative AI-powered chatbot provides factual responses for regulatory compliance.
Which solution prevents the underlying foundation model (FM) from hallucinating?
A. Use AWS Config to query compliance metadata by using natural language.
B. Configure Amazon Bedrock Guardrails to evaluate user inputs and model responses.
C. Use Amazon Fraud Detector to detect potentially fraudulent online activities.
D. Use AWS Audit Manager to prepare IT audit and compliance reports.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock Guardrails can evaluate user inputs and model responses and enforce safety and factuality policies. With automated reasoning capabilities, Guardrails help reduce and prevent factual hallucinations for compliance-sensitive use cases. The other services (AWS Config, Amazon Fraud Detector, and AWS Audit Manager) do not prevent foundation model hallucinations.
Question 200
A documentary filmmaker wants to reach more viewers. The filmmaker wants to automatically add subtitles and voice-overs in multiple languages to their films.
Which combination of steps will meet these requirements? (Choose two.)
A. Use Amazon Transcribe and Amazon Translate to generate subtitles in other languages.
B. Use Amazon Textract and Amazon Translate to generate subtitles in other languages.
C. Use Amazon Polly to generate voice-overs in other languages.
D. Use Amazon Translate to generate voice-overs in other languages.
E. Use Amazon Textract to generate voice-overs in other languages.
Show Answer
Correct Answer: A, C
Explanation: Amazon Transcribe converts speech in the documentary into text, and Amazon Translate translates that text into other languages to create subtitles. Amazon Polly converts the translated text into natural-sounding speech for multilingual voice-overs. Amazon Textract extracts text from documents/images and is not used for audio transcription or voice generation. Amazon Translate translates text only and does not synthesize speech.
Question 201
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company’s private network.
Which solution will meet these requirements?
A. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.
B. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.
C. Use AWS PrivateLink to connect the VPC and Amazon Bedrock.
D. Use AWS Key Management Service (AWS KMS) keys to encrypt the data.
Show Answer
Correct Answer: C
Explanation: AWS PrivateLink provides private connectivity from a VPC to supported AWS services, including Amazon Bedrock, so traffic does not traverse the public internet. This allows the company to access Bedrock while keeping network traffic within its private network. IAM controls authorization but does not provide private network connectivity, and AWS KMS encrypts data but does not keep traffic private.
Question 202
Which option is an example of unsupervised learning?
A. Clustering data points into groups based on their similarity
B. Training a model to recognize images of animals
C. Predicting the price of a house based on the house’s features
D. Generating human-like text based on a given prompt
Show Answer
Correct Answer: A
Explanation: Unsupervised learning works with unlabeled data to discover underlying structure or patterns. Clustering groups similar data points without predefined labels. Image recognition and house price prediction are supervised learning tasks, while generating text is typically done with generative language models rather than serving as the standard example of unsupervised learning.
Question 203
A company is monitoring a predictive model by using Amazon SageMaker Model Monitor. The company notices data drift beyond a defined threshold. The company wants to mitigate a potentially adverse impact on the predictive model.
Which solution will meet these requirements?
A. Restart the SageMaker AI endpoint.
B. Adjust the monitoring sensitivity.
C. Re-train the model with fresh data.
D. Set up experiments tracking.
Show Answer
Correct Answer: C
Explanation: Amazon SageMaker Model Monitor detects data drift, but restarting an endpoint or changing monitoring sensitivity does not correct degraded model performance caused by distribution changes. Setting up experiments helps track runs but does not mitigate drift. The appropriate mitigation is to retrain the model on fresh, representative data and redeploy the updated model.
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