Attempt PFRDA Grade A 2026 AI/ML Practice Quiz and Download PDF

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.

What is the AI/ML syllabus for PFRDA Grade A 2026?

Artificial Intelligence and Machine Learning is one of the five new areas added to the PFRDA Grade A General Stream Paper 2 syllabus in 2026. The topic is focused mainly on conceptual and application-oriented areas rather than advanced programming or mathematical derivations. Candidates should understand how different AI/ML techniques work, where they are used, and how they differ from one another.

AI/ML Area	Important Topics
Machine Learning	Supervised, unsupervised and reinforcement learning
Data & Models	Data preprocessing, model training and evaluation
ML Concepts	Overfitting, underfitting, bias-variance and cross-validation
NLP	NLP, text processing and sentiment analysis
LLMs	Transformers, attention, prompts, embeddings and RAG
Generative AI	Text, image, audio, video and multimodal AI
Agentic AI	AI agents, planning, tools, memory and actions
Responsible AI	Fairness, transparency, privacy, safety and accountability

What are the important Machine Learning concepts for PFRDA Grade A?

Machine Learning enables computer systems to learn patterns from data and use those patterns to make predictions or decisions. Candidates should understand the basic ML lifecycle, including data collection, preprocessing, model training, evaluation, deployment and monitoring. The difference between classification and regression, as well as training, validation and test data, should also be clear. Important Machine Learning concepts include:

• Meaning and applications of Machine Learning
• Machine Learning lifecycle
• Features and target variables
• Training, validation and test data
• Parameters and hyperparameters
• Classification and regression
• Training and inference
• Generalisation of ML models
• Model deployment and monitoring

Also Check: How to prepare AI/ML for PFRDA Grade A

What are the types of Machine Learning?

Machine Learning can broadly be divided into supervised learning, unsupervised learning and reinforcement learning. The main difference is the type of data or feedback used during the learning process. Candidates should understand the purpose, basic working and common applications of each type.

Type	Key Idea	Example
Supervised Learning	Learns from labelled data	Fraud detection
Unsupervised Learning	Finds patterns in unlabelled data	Customer segmentation
Reinforcement Learning	Learns using rewards and penalties	Robot navigation

What are the important Supervised Learning topics?

Supervised Learning uses labelled datasets where the expected output is already known. The model learns the relationship between input variables and known outcomes and then uses this learning to make predictions on new data. Candidates should understand the difference between classification and regression and the basic purpose of commonly used algorithms. Important areas include:

• Classification
• Binary classification
• Multiclass classification
• Regression
• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine
• K-Nearest Neighbours
• Naive Bayes
• Gradient Boosting

What are the important Unsupervised Learning topics?

Unsupervised Learning works with data that does not have predefined labels. It is mainly used to identify hidden patterns, groups or relationships within a dataset. Candidates should focus on clustering, dimensionality reduction and association analysis and understand their practical applications. Important topics include:

• Unlabelled datasets
• Clustering
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Dimensionality reduction
• Association analysis
• Market basket analysis
• Customer segmentation

What should candidates study under Data Preprocessing?

Data preprocessing prepares raw data before it is given to a Machine Learning model. Poor-quality data can affect model performance, which is why concepts such as missing values, duplicate records, outliers and data transformation are important. Candidates should also understand feature selection, encoding, scaling and the handling of imbalanced datasets. Key areas for preparation are:

• Data cleaning
• Missing values
• Duplicate records
• Outliers
• Categorical data
• Encoding
• Normalisation
• Standardisation
• Feature selection
• Feature extraction
• Data transformation
• Imbalanced datasets
• Train-test split
• Garbage In, Garbage Out (GIGO)

What are the important Model Evaluation concepts?

Model evaluation helps determine how well a Machine Learning model performs on data. Candidates should understand common classification metrics such as accuracy, precision, recall, specificity and F1 Score. For regression, basic knowledge of MAE, MSE, RMSE and R-squared is important.

Area	Important Concepts
Classification	Accuracy, Precision, Recall, Specificity, F1 Score
Confusion Matrix	TP, TN, FP, FN
Curve-based Metrics	ROC Curve, AUC
Regression	MAE, MSE, RMSE, R-squared
Model Performance	Training vs testing performance

What are Overfitting and Underfitting in Machine Learning?

Overfitting occurs when a model learns the training data too specifically and performs poorly on unseen data. Underfitting occurs when the model is too simple to identify important patterns in the data. Candidates should understand their causes, signs and methods used to improve model performance.

Concept	Meaning	Common Solution
Overfitting	Model performs well on training data but poorly on new data	Cross-validation, regularisation, more data
Underfitting	Model performs poorly on both training and testing data	Increase complexity, add useful features

Candidates should also revise the bias-variance trade-off, where high bias is generally linked with underfitting and high variance with overfitting.

What is Cross-Validation in Machine Learning?

Cross-validation is a technique used to check how well a model is likely to perform on unseen data. In K-Fold Cross-Validation, the dataset is divided into K parts and the model is trained and evaluated multiple times using different portions of the data. Candidates should understand the purpose and basic working of K-Fold, Stratified K-Fold and Leave-One-Out Cross-Validation. Important concepts include:

• Meaning of cross-validation
• K-Fold Cross-Validation
• Stratified K-Fold
• Leave-One-Out Cross-Validation
• Training-validation-test concept
• Advantages and limitations of cross-validation

Check: What are the major changes in the PFRDA Grade A 2026

What are the important NLP topics for PFRDA Grade A?

Natural Language Processing (NLP) is a branch of AI that enables computers to process and understand human language. Candidates should understand basic text-processing techniques and common NLP applications. The syllabus should also be studied in the context of modern AI systems such as chatbots and language models. Important NLP topics include:

• Meaning of NLP
• NLP pipeline
• Tokenization
• Stop-word removal
• Stemming
• Lemmatization
• Part-of-Speech tagging
• Named Entity Recognition
• Text classification
• Word embeddings
• Sentiment analysis
• Chatbots
• Machine translation
• Text summarisation
• Question answering

What is Sentiment Analysis?

Sentiment Analysis is an NLP application used to identify the opinion or attitude expressed in text. It commonly classifies text as positive, negative or neutral. Candidates should also understand advanced concepts such as emotion detection and aspect-based sentiment analysis, along with limitations caused by sarcasm, context and multilingual language. Common applications include:

• Customer feedback analysis
• Product-review analysis
• Social-media monitoring
• Brand monitoring
• Market research

What are Large Language Models or LLMs?

Large Language Models are AI models trained on large amounts of text data to understand and generate human-like language. Candidates should understand basic concepts such as tokens, parameters, context windows, prompts, training and inference. They should also be familiar with the Transformer architecture, attention, self-attention, embeddings, fine-tuning and Retrieval-Augmented Generation (RAG).

LLM Concept	Basic Meaning
Token	A unit of text processed by the model
Parameter	Learned value used by the model
Context Window	Amount of information the model can consider
Prompt	Input or instruction given to the model
Embedding	Numerical representation of information
Fine-Tuning	Further training for a specific task
RAG	Uses external information to improve generated responses

What are the important Generative AI concepts?

Generative AI refers to AI systems that can create new content such as text, images, audio, video and code. Candidates should understand the difference between generative and discriminative AI and have basic awareness of foundation models and multimodal AI. The risks associated with AI-generated content should also be studied. Important topics include:

• Generative AI
• Generative vs discriminative AI
• Foundation models
• Text generation
• Image generation
• Audio generation
• Video generation
• Code generation
• Multimodal AI
• Prompt engineering
• Synthetic data
• AI-generated content
• Deepfakes and misinformation
• Copyright concerns
• Hallucinations

What is Agentic AI and why is it important for PFRDA Grade A?

Agentic AI refers to AI systems that can plan tasks, make decisions and take actions to achieve a particular goal. Unlike a system that only responds to a single prompt, an AI agent can work through multiple steps and use tools or external systems. Candidates should understand the basic agent workflow and the risks associated with autonomous actions. Important areas include:

• AI agents
• Autonomous and semi-autonomous agents
• Goals
• Perception and input
• Reasoning
• Planning
• Memory
• Tools
• Actions
• Feedback
• Tool calling
• Task decomposition
• Workflow automation
• Single-agent systems
• Multi-agent systems
• Human-in-the-loop systems

What is Responsible AI?

Responsible AI focuses on developing and using AI systems in a safe, fair, transparent and accountable manner. This is especially relevant for regulated sectors where automated decisions can affect customers and organisations. Candidates should understand AI bias, explainability, privacy, security and the role of human oversight.

Responsible AI Area	What to Understand
Fairness	Avoiding unfair or discriminatory outcomes
Accountability	Assigning responsibility for AI decisions
Transparency	Making AI systems and decisions understandable
Explainability	Understanding why a model produced an output
Privacy	Protecting personal and sensitive data
Security	Protecting AI systems from attacks and misuse
Human Oversight	Keeping appropriate human control over AI decisions
Reliability & Safety	Ensuring dependable and safe AI outputs

What are the important Reinforcement Learning concepts?

Reinforcement Learning allows an agent to learn through interaction with an environment. Instead of using labelled data, the agent receives rewards or penalties based on its actions and learns to improve its decisions. Candidates should understand the basic framework and terms such as state, action, reward, policy and value. Important topics include:

• Agent
• Environment
• State
• Action
• Reward
• Policy
• Value
• Episode
• Exploration vs exploitation
• Q-Learning
• Deep Q-Networks
• Policy-based approaches

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