AI-102 Practice Test Overview: Your Roadmap to Success
This practice test is designed to help you ace the AI-102 exam and land your dream job in AI. It's not some robotic, overly-technical ordeal. Think of it as a friendly sparring partner, helping you hone your skills before the big day.
Total Questions: 100 (various question types - multiple choice, true/false, fill-in-the-blank, and short answer)
Target Job Roles: This test will benefit professionals aiming for roles such as:
- AI Engineer/Developer: Building and deploying AI models.
- Machine Learning Engineer: Focusing on model training and optimization.
- Data Scientist: Analyzing data to extract meaningful insights and build AI solutions.
- AI Consultant: Advising businesses on AI implementation and strategy.
Language: English
Key Coverage Areas: The practice test comprehensively covers the following areas:
I. Fundamentals of Artificial Intelligence (25 Questions)
- What is AI? – Definitions, types (narrow/general), and applications. (Think Siri, self-driving cars—that's AI in action!)
- Ethical Considerations in AI – Bias, fairness, accountability, and societal impact. (We'll explore tricky situations here — imagine an AI loan application unfairly rejecting someone.)
- History of AI – Key milestones and influential figures. (We'll touch upon the pioneers who laid the foundation.)
II. Machine Learning (35 Questions)
- Supervised Learning: Regression (linear, logistic), classification (decision trees, support vector machines, naive Bayes). Example: Predicting house prices based on size and location.
- Unsupervised Learning: Clustering (k-means, hierarchical), dimensionality reduction (PCA). Example: Grouping customers based on purchasing behavior.
- Reinforcement Learning: Concepts, algorithms (Q-learning), applications. Think of a robot learning to walk—trial and error, reward-based learning.
- Model Evaluation Metrics: Accuracy, precision, recall, F1-score, AUC. (We'll decipher the numbers and understand what they really mean.)
- Model Selection and Hyperparameter Tuning: Cross-validation, grid search, random search. (Finding the best settings for your model is key!)
III. Deep Learning (25 Questions)
- Neural Networks: Architecture, activation functions, backpropagation. (We'll demystify the inner workings of neural networks.)
- Convolutional Neural Networks (CNNs): Image classification, object detection. (Think image recognition – your phone using this every day!)
- Recurrent Neural Networks (RNNs): Sequence modeling (natural language processing, time series analysis). (Understanding how AI processes text and sequential data.)
IV. Natural Language Processing (NLP) (10 Questions)
- Tokenization, stemming, lemmatization: Text preprocessing techniques. (Cleaning up the data before analysis – it's crucial!)
- Word embeddings (Word2Vec, GloVe): Representing words as vectors. (Understanding the semantic relationships between words.)
- Sentiment analysis: Determining the emotional tone of text.
V. Deployment and Monitoring (5 Questions)
- Model deployment strategies: Cloud platforms (AWS, Azure, GCP), on-premise deployments. (Getting your model out into the world!)
- Model monitoring and maintenance: Tracking performance, retraining models. (AI isn’t a “set it and forget it” scenario.)
This detailed outline provides a clear path for your AI-102 preparation. Good luck! Remember to practice consistently and stay focused. You've got this!
