The PFRDA Grade A 2026 AI/ML Practice Quiz is designed to help candidates revise the newly added Artificial Intelligence and Machine Learning topics in the General Stream Paper 2 syllabus. AI/ML is one of the major additions to the 2026 syllabus, making it important for candidates to understand both basic concepts and their practical applications.
The quiz covers topics such as machine learning, data preprocessing, model evaluation, NLP, LLMs, Generative AI, Agentic AI, reinforcement learning, and Responsible AI. Regular practice can help candidates improve conceptual clarity and accuracy while preparing for the Paper 2 exam.
Download PFRDA Grade A AI/ML Practice Quiz PDF
The PFRDA Grade A AI/ML Practice Quiz PDF provides practice questions based on the major AI and Machine Learning concepts included in the 2026 General Stream syllabus. Candidates can use the PDF for quick revision and practice after completing the basic concepts.
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Attempt PFRDA Grade A AI/ML Practice Quiz
Attempt the free AI/ML practice quiz to check your preparation level and identify topics that need more revision. Since AI/ML has been newly added to the PFRDA Grade A 2026 syllabus, candidates should focus on understanding concepts instead of relying only on memorisation.
1. Which statement best describes Artificial Intelligence (AI)?
2. Which relationship among AI, Machine Learning and Deep Learning is most accurate?
3. In traditional rule-based programming, how are outputs primarily produced?
4. In machine learning, what is a feature?
5. What is meant by inference in a machine-learning system?
6. Which of the following is primarily a regression task?
7. Which of the following is primarily a classification task?
8. What is a model parameter in machine learning?
9. What is a hyperparameter?
10. What does model generalization refer to?
11. What is the defining characteristic of supervised learning?
12. Which algorithm is most directly associated with predicting a continuous target using a linear relationship?
13. Despite its name, Logistic Regression is commonly used for which task?
14. What is a decision tree designed to do?
15. A Random Forest primarily improves upon a single decision tree by:
16. What is the basic idea behind K-Nearest Neighbours (KNN)?
17. Naive Bayes classifiers rely on which simplifying assumption?
18. What is the key objective of a Support Vector Machine classifier?
19. Which method is an ensemble technique that builds learners sequentially to correct earlier errors?
20. A bank has historical customer data with a known label ‘churned’ or ‘not churned’. Which learning setup is most appropriate?
Quiz Summary
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What is the AI/ML syllabus for PFRDA Grade A 2026?
Artificial Intelligence and Machine Learning is one of the new areas added to the PFRDA Grade A General Stream Paper 2 syllabus in 2026. The syllabus covers basic and important concepts of Machine Learning, different types of learning, data preprocessing, model evaluation, NLP, sentiment analysis, LLMs, Agentic AI, Responsible AI, Cross-Validation, Overfitting and Underfitting, and Reinforcement Learning. Candidates should focus on understanding the meaning, basic working and applications of these concepts.
| AI/ML Area | Important Topics |
|---|---|
| Machine Learning | Machine Learning, Introduction to ML Model |
| Learning Types | Supervised Learning, Unsupervised Learning, Reinforcement Learning |
| Data & Models | Data Preprocessing, Model Evaluation |
| ML Concepts | Overfitting, Underfitting, Cross-Validation |
| NLP | NLP, Sentiment Analysis |
| Modern AI | LLM, Agentic AI |
| AI Ethics | Responsible AI |
What is Machine Learning?
Machine Learning is a part of Artificial Intelligence that allows computers to learn from data and improve their performance without being directly programmed for every task. Candidates should understand the basic meaning of Machine Learning and the introduction to ML models. The focus should be on understanding how Machine Learning models use data to identify patterns and produce results.
What is Supervised Learning?
Supervised Learning is a type of Machine Learning in which a model learns from labelled data. The model uses the available input and known output to learn patterns and make predictions. Candidates should understand the basic concept and working of Supervised Learning.
What is Unsupervised Learning?
Unsupervised Learning is a type of Machine Learning in which a model works with data without predefined labels. It helps identify patterns or relationships within the available data. Candidates should understand the basic concept and working of Unsupervised Learning.
Also Check: How to prepare AI/ML for PFRDA Grade A
What is Data Preprocessing?
Data Preprocessing refers to preparing data before it is used by a Machine Learning model. It helps make the data suitable for analysis and model development. Candidates should understand the basic purpose and importance of Data Preprocessing in Machine Learning.
What is Model Evaluation?
Model Evaluation is used to understand how well a Machine Learning model performs. It helps determine whether a model is producing suitable results based on the available data. Candidates should understand the basic concept and purpose of evaluating an ML model.
What are Overfitting and Underfitting?
Overfitting occurs when a Machine Learning model learns the training data too closely and does not perform well on new data. Underfitting occurs when a model is not able to learn the important patterns in the data. Candidates should understand the difference between Overfitting and Underfitting and their impact on model performance.
| Concept | Meaning |
|---|---|
| Overfitting | The model learns the training data too closely and performs poorly on new data. |
| Underfitting | The model fails to learn the important patterns in the data. |
What is Cross-Validation in Machine Learning?
Cross-Validation is a technique used to evaluate the performance of a Machine Learning model. It involves using different parts of the available data for training and evaluation to get a better understanding of model performance. Candidates should understand the basic concept and purpose of Cross-Validation.
What is NLP in AI/ML?
Natural Language Processing (NLP) is a field of Artificial Intelligence that enables computers to work with and understand human language. It is used in systems that process or analyse text and other forms of human language. Candidates should understand the basic concept and applications of NLP.
What is Sentiment Analysis?
Sentiment Analysis is an application of Natural Language Processing used to identify the sentiment or opinion expressed in text. It can help determine whether a piece of text expresses a positive, negative or neutral sentiment. Candidates should understand the basic concept and use of Sentiment Analysis.
What are Large Language Models (LLMs)?
Large Language Models (LLMs) are AI models designed to understand and generate human language. They are used in various applications involving language and text. Candidates should understand the basic concept of LLMs and their role in modern AI systems.
Check: What are the major changes in the PFRDA Grade A 2026
What is Agentic AI?
Agentic AI refers to AI systems that can work towards a specific goal by making decisions and carrying out actions. Candidates should understand the basic concept of Agentic AI and how it differs from AI systems that only provide responses to user inputs.
What is Responsible AI?
Responsible AI refers to the safe and responsible development and use of Artificial Intelligence systems. It focuses on ensuring that AI is developed and used appropriately. Candidates should understand the basic concept and importance of Responsible AI.
What is Reinforcement Learning?
Reinforcement Learning is a type of Machine Learning in which an agent learns through interaction and feedback. The agent learns from the results of its actions and improves its decisions over time. Candidates should understand the basic concept and working of Reinforcement Learning.
FAQs
Yes, AI/ML is one of the new topics added to the General Stream Paper 2 syllabus in 2026.
Candidates should study Machine Learning, Supervised and Unsupervised Learning, Data Preprocessing, Model Evaluation, NLP, Sentiment Analysis, Overfitting and Underfitting, LLMs, Agentic AI, Responsible AI, Cross-Validation, Introduction to ML Models, and Reinforcement Learning.
Naive Bayes is a supervised Machine Learning algorithm that uses probability to classify data into different categories based on the available features.
The major types are supervised learning, unsupervised learning and reinforcement learning.
Candidates should revise fairness, accountability, transparency, explainability, privacy, security, safety and human oversight.
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