A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
A. Review the training data to check for biases. Include data from all demographics in the training data.
B. Use a deep learning model with many hidden layers.
C. Keep the model’s decision-making process a secret to protect proprietary algorithms.
D. Continuously monitor the model’s performance on a static test dataset
Show Answer
Correct Answer: A
Explanation: Responsible and fair AI for loan approval requires identifying and mitigating bias in the training data. Reviewing the dataset for bias and ensuring appropriate representation across demographics helps reduce unfair outcomes. Using a deeper model does not improve fairness, hiding decision-making reduces transparency, and monitoring only on a static test set is insufficient for ensuring ongoing fairness.
Question 145
A company needs to collect a large dataset to train an AI assistant in a specific content area.
Which dataset will meet this requirement?
A. Diverse conversations that use relevant terminology
B. Time series data of general purpose historical sales
C. Sentiment analysis of news articles
D. Unique product IDs and corresponding user IDs
Show Answer
Correct Answer: A
Explanation: An AI assistant for a specific content area is best trained on a large corpus of diverse conversational data that uses the relevant domain terminology. This teaches the model domain-specific language, context, and dialogue patterns. The other options are datasets suited to forecasting, sentiment analysis, or identifier mapping rather than conversational assistant training.
Question 146
A news agency publishes articles in English. The agency wants to make articles available in other languages.
Which solution meets these requirements?
A. Add Amazon Transcribe to the company’s website.
B. Use the Amazon Translate real-time translation feature.
C. Add Amazon Personalize to the company’s website.
D. Use the Amazon Textract real-time document processing feature.
Show Answer
Correct Answer: B
Explanation: Amazon Translate is the AWS service designed to translate text between languages in real time, making English news articles available in multiple languages. Amazon Transcribe converts speech to text, Amazon Personalize provides recommendations, and Amazon Textract extracts text and data from documents rather than translating them.
Question 147
A company has guidelines for data storage and deletion.
Which data governance strategy does this describe?
A. Data de-identification
B. Data quality standards
C. Data retention
D. Log storage
Show Answer
Correct Answer: C
Explanation: Guidelines governing how long data is stored and when it must be deleted are part of a data retention strategy. Data de-identification focuses on anonymizing data, data quality standards ensure data accuracy and consistency, and log storage specifically concerns storing logs rather than overall data lifecycle policies.
Question 148
An AI practitioner has trained a model on a training dataset. The model performs well on the training data. However, the model does not perform well on evaluation data.
What is the MOST likely cause of this issue?
A. The model is underfit.
B. The model requires prompt engineering.
C. The model is biased.
D. The model is overfit.
Show Answer
Correct Answer: D
Explanation: A model that performs well on the training data but poorly on unseen evaluation data is most likely overfitting. It has learned patterns specific to the training set, including noise, and does not generalize well. Underfitting would typically result in poor performance on both training and evaluation data. Prompt engineering is not relevant to a traditionally trained model's generalization issue, and bias alone does not specifically explain the train/evaluation performance gap.
Question 149
What does inference refer to in the context of AI?
A. The process of creating new AI algorithms
B. The use of a trained model to make predictions or decisions on unseen data
C. The process of combining multiple AI models into one model
D. The method of collecting training data for AI systems
Show Answer
Correct Answer: B
Explanation: Inference is the phase after training in which a trained AI model is used to process new, unseen inputs and generate predictions, classifications, or decisions. It does not involve creating algorithms, merging models, or collecting training data.
Question 150
A media streaming platform wants to provide movie recommendations to users based on the users’ account history.
Which AWS service meets these requirements?
A. Amazon Polly
B. Amazon Comprehend
C. Amazon Transcribe
D. Amazon Personalize
Show Answer
Correct Answer: D
Explanation: Amazon Personalize is the AWS machine learning service designed to build personalized recommendations based on user behavior and account history. Amazon Polly is for text-to-speech, Amazon Comprehend is for natural language processing, and Amazon Transcribe converts speech to text.
Question 151
A company wants to assess internet quality in remote areas of the world. The company needs to collect internet speed data and store the data in Amazon RDS. The company will analyze internet speed variation throughout each day. The company wants to create an AI model to predict potential internet disruptions.
Which type of data should the company collect for this task?
A. Tabular data
B. Text data
C. Time series data
D. Audio data
Show Answer
Correct Answer: C
Explanation: The correct answer is C. Internet speed measurements collected over time with timestamps are time series data. Because the goal is to analyze variation throughout the day and predict future internet disruptions, the temporal ordering of observations is essential. Although the data can be stored in Amazon RDS as rows in tables, the data type for the machine learning task is time series.
Question 152
A company has an ML model. The company wants to know how the model makes predictions.
Which term refers to understanding model predictions?
A. Model interpretability
B. Model training
C. Model interoperability
D. Model performance
Show Answer
Correct Answer: A
Explanation: Model interpretability is the concept of understanding how an ML model arrives at its predictions, including which features influence outputs and the reasoning behind decisions. The other options refer to building the model, compatibility between systems, or evaluation metrics rather than explaining predictions.
Question 153
Which scenario represents a practical use case for generative AI?
A. Using an ML model to forecast product demand
B. Employing a chatbot to provide human-like responses to customer queries in real time
C. Using an analytics dashboard to track website traffic and user behavior
D. Implementing a rule-based recommendation engine to suggest products to customers
Show Answer
Correct Answer: B
Explanation: Generative AI is designed to create new content such as natural language. A chatbot that generates human-like responses to customer queries is a classic practical application of generative AI. The other options describe predictive machine learning, analytics, or rule-based systems rather than generative AI.
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