A company wants to integrate an AI solution to contact emergency services within 30 seconds of vehicle crash detection. The company wants to use a pre-trained model without additional training.
Which factor should the company prioritize when selecting a model to meet these requirements?
A. Model customization
B. Model size
C. Model cost
D. Model temperature
Show Answer
Correct Answer: B
Explanation: The key requirement is responding within 30 seconds using a pre-trained model. The most important consideration is inference speed (latency). Since latency is not an answer choice, model size is the best proxy because smaller models generally have lower inference latency and can produce results more quickly. Model customization is unnecessary because no additional training is desired, model cost does not address the time requirement, and temperature only affects output randomness, not the ability to meet strict response-time constraints.
Question 115
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.
Which solution will meet these requirements with the LEAST operational effort?
A. Create a custom model training job in PartyRock on Amazon Bedrock.
B. Use Amazon SageMaker JumpStart to create a training job.
C. Use a custom script to run an Amazon SageMaker AI model training job.
D. Create a Jupyter notebook on an Amazon EC2 instance. Use the notebook to train the model.
Show Answer
Correct Answer: B
Explanation: Amazon SageMaker JumpStart provides managed access to many open-source foundation models and built-in fine-tuning workflows, minimizing infrastructure setup and operational overhead. PartyRock is not intended for custom model training, while custom SageMaker scripts and EC2-hosted notebooks require more operational effort.
Question 116
A company uses Amazon Comprehend to analyze customer feedback. A customer has several unique trained models. The company uses Comprehend to assign each model an endpoint. The company wants to automate a report on each endpoint that is not used for more than 15 days.
Which service will meet these requirements?
A. AWS Trusted Advisor
B. Amazon CloudWatch
C. AWS CloudTrail
D. AWS Config
Show Answer
Correct Answer: A
Explanation: AWS Trusted Advisor includes a specific cost optimization check for Amazon Comprehend underutilized endpoints. It identifies endpoints that have not been used for more than 15 days, matching the requirement to automate reporting on unused endpoints. While CloudWatch provides endpoint metrics and monitoring, the 15-day underutilized endpoint report is a built-in Trusted Advisor check.
Sources:
https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints-monitor.html
Question 117
A company is using Amazon Bedrock to build an AI assistant. The AI assistant helps customers find relevant products by making suggestions. However, the AI assistant's responses are often generic and irrelevant. The company wants to use prompt engineering to improve the AI assistant's responses.
Which solution will meet these requirements?
A. Use few-shot prompting to add domain-specific context and explicit instructions.
B. Use chain-of-thought prompting with hidden reasoning steps to ignore explicit domain instructions.
C. Modify the AI assistant's conversational style to use more formal language and include technical product specifications.
D. Use zero-shot prompting to augment retrieval from a product database.
Show Answer
Correct Answer: A
Explanation: Few-shot prompting improves response relevance by providing domain-specific examples and explicit instructions that guide the model toward the desired behavior and output format. Chain-of-thought is not intended to replace domain guidance, changing style does not address relevance, and zero-shot prompting does not augment retrieval from a product database.
Question 118
A healthcare company wants to create a model to improve disease diagnostics by analyzing patient voices. The company has recorded hundreds of patient voices for this project.
The company is currently filtering voice recordings according to duration and language.
Which phase of the ML lifecycle describes the current project phase?
A. Data collection
B. Data preprocessing
C. Feature engineering
D. Model training
Show Answer
Correct Answer: B
Explanation: Filtering voice recordings by duration and language is a data preprocessing task. It involves cleaning and selecting usable data according to defined criteria before feature extraction or model training. Data collection is the recording phase, feature engineering extracts meaningful attributes from the audio, and model training fits the model using the prepared data.
Question 119
A company stores customer personally identifiable information (PII) data. The company must store the PII data within the company's AWS Region.
Which aspect of governance does this describe?
A. Data mining
B. Data residency
C. Pre-training bias
D. Geolocation routing
Show Answer
Correct Answer: B
Explanation: The requirement that PII must be stored within the company's AWS Region is a data residency requirement. Data residency governs the geographic location where data is stored to meet legal, regulatory, or organizational policies. The other options do not describe storage location governance.
Question 120
A company wants to develop an interpretable ML model to assess the risk of loan applications.
Which type of ML model or algorithm will meet these requirements?
A. Deep learning model
B. Logistic regression model
C. K-means algorithm
D. Random cut forest algorithm
Show Answer
Correct Answer: B
Explanation: A logistic regression model is a standard interpretable supervised learning algorithm for binary risk prediction. Its coefficients show how each feature influences the predicted odds, making it suitable for regulated use cases such as loan risk assessment. Deep learning models are generally less interpretable, k-means is an unsupervised clustering algorithm rather than a classifier, and random cut forest is primarily used for anomaly detection.
Question 121
A company wants to customize Amazon Bedrock foundation models (FMs) to improve an application's performance. The company must prepare a training dataset for text-to-text model fine-tuning.
Which dataset format should the company use to train the models?
A. A JSON file with labeled data
B. A CSV file with unlabeled data
C. A CSV file with tabular data
D. A text file with unlabeled data
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock text-to-text model fine-tuning requires labeled prompt-completion examples in JSON Lines (JSONL) format, where each record is a JSON object containing the input and desired output. CSV or unlabeled text datasets are not the required format for this fine-tuning workflow.
Question 122
A company wants to classify images of different objects based on custom features extracted from a dataset.
Which solution will meet this requirement with the LEAST development effort?
A. Use traditional ML algorithms with custom features extracted from the dataset.
B. Use a pre-trained deep learning model. Fine-tune the model on the dataset.
C. Use a generative adversarial network (GAN) model to classify the images.
D. Use a support vector machine (SVM) with manually engineered features for classification.
Show Answer
Correct Answer: B
Explanation: A pre-trained deep learning model that is fine-tuned on the custom image dataset uses transfer learning, which minimizes development effort while achieving strong image classification performance. The alternatives require manual feature engineering or are not appropriate for classification (GANs are primarily for data generation).
Question 123
HOTSPOT
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A company wants to build generative AI applications by using Amazon Bedrock. The company wants to minimize development effort.
Select and order the model development techniques from the following list from the LEAST development effort to the MOST development effort. Each model development technique should be selected one time.
Explanation: Using a foundation model with prompt engineering requires the least effort. RAG adds retrieval infrastructure but no model training. Fine-tuning requires curated training data and model adaptation. Continued pre-training requires substantial domain data and additional training, making it the most development-intensive.
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