AI-901 Practice Test

Answer choices

1. A. Named entity recognition
2. B. Automated ML forecasting
3. C. Image Analysis
4. D. Speech translation

Wrong-answer review

• A. Named entity recognition: Named entity recognition extracts people, places, organizations, and other entities from text, not photo captions or tags.
• B. Automated ML forecasting: Automated ML forecasting predicts future numeric or time-series values and is not an image-analysis feature.
• D. Speech translation: Speech translation translates spoken audio between languages, not product photos.

Answer choices

1. A. Allow facial recognition only during business hours
2. B. Train the model on a diverse dataset that includes people from various ethnicities and ages
3. C. Ignore potential biases in the data as unlikely to affect security
4. D. Use only employee images for training

Answer choices

1. A. A model that fails to capture the underlying patterns in the data, leading to poor performance on both training and validation data
2. B. A model that performs well on both training and validation data
3. C. A model that overgeneralizes to new data, performing poorly
4. D. A model that makes predictions with high accuracy but is overly complex

Answer choices

1. A. Designing a user interface for a chatbot
2. B. Writing code to integrate AI services into an application
3. C. Developing algorithms to improve image recognition accuracy
4. D. Creating a set of instructions to guide the generation of text by a language model

Answer choices

1. A. Speech-to-text, text-to-speech, and speaker recognition
2. B. Pipelines, compute clusters, and model registries
3. C. Image captioning, object detection, and OCR
4. D. Sentiment analysis, key phrase extraction, named entity recognition, and language detection

Answer choices

1. A. Serverless workflow orchestration
2. B. Batch model retraining
3. C. Sentiment analysis
4. D. Built-in anomaly detection

Answer choices

1. A. To define the training parameters for algorithms
2. B. To monitor the performance of deployed models
3. C. To provide input data for model training and testing
4. D. To store and manage machine learning models

Answer choices

1. A. Providing real-world examples or references to help the model understand and generate more grounded text
2. B. Creating a database to store information about different domains
3. C. The process of integrating physical sensors into a machine learning model
4. D. Designing algorithms for autonomous vehicle navigation

Answer choices

1. A. Sentiment analysis
2. B. Face detection
3. C. Optical character recognition
4. D. Speaker diarization

Answer choices

1. A. Applying a fixed tax formula to an invoice total
2. B. Routing a request through a hard-coded approval matrix
3. C. Rejecting passwords that do not match a required pattern
4. D. Generating personalized recommendations based on user behavior