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100+ Free Assam HS Skill Subject Artificial Intelligence Practice Questions

Prepare for the Assam State School Education Board (ASSEB/AHSEC) Higher Secondary Skill Subject Artificial Intelligence Examination exam with instant access — no signup required.

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2026 Statistics

Key Facts: Assam HS Skill Subject Artificial Intelligence Exam

50 / 50

Marks split between theory written paper and practical/project work

ASSEB Board Regulations

2 Hours

Duration of the Higher Secondary AI skill subject theory paper

ASSEB Examination Scheme

30%

Minimum pass percentage required in theory and practical components

ASSEB Board Guidelines

100

Curriculum-aligned practice MCQs provided in this OpenExamPrep bank

OpenExamPrep

Assam ASSEB HS Skill AI assesses Class 11-12 students on foundational Artificial Intelligence concepts, Python data libraries, ML model evaluation, computer vision, natural language processing, and AI ethics.

Sample Assam HS Skill Subject Artificial Intelligence Practice Questions

Try these sample questions to test your Assam HS Skill Subject Artificial Intelligence exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1What fundamental characteristic distinguishes an Artificial Intelligence (AI) system from a conventional computer program?
A.Ability to learn from data and adapt without explicit step-by-step programming for every scenario
B.Capability to execute arithmetic operations at megahertz processor speeds
C.Storage of large volumes of relational database tables on physical storage media
D.Requirement of manual user input for every logical decision
Explanation: AI systems are designed to learn patterns from data, generalize to unseen situations, and make autonomous decisions or predictions without requiring explicit hardcoded rules for every scenario.
2Which diagrammatic relationship correctly describes the structural containment between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
A.Deep Learning is a subset of Machine Learning, which is a subset of Artificial Intelligence
B.Machine Learning is a subset of Deep Learning, which is a subset of Artificial Intelligence
C.Artificial Intelligence is a subset of Machine Learning, which is a subset of Deep Learning
D.Deep Learning, Machine Learning, and Artificial Intelligence are three independent non-overlapping domains
Explanation: Artificial Intelligence is the broad umbrella field. Machine Learning is a specific subset of AI focused on statistical learning from data. Deep Learning is a specialized sub-field of ML using multi-layered artificial neural networks.
3Which three core operational domains are recognized in the foundational AI framework?
A.Data, Computer Vision (CV), and Natural Language Processing (NLP)
B.Robotics, Quantum Computing, and Virtual Reality
C.Hardware Design, Networking, and Database Management
D.Web Development, Cloud Computing, and Cyber Security
Explanation: The foundational AI framework categorizes AI applications into three primary data-driven domains: Data (numeric/tabular data analytics), Computer Vision (visual data like images/videos), and Natural Language Processing (human text/speech data).
4Alan Turing proposed the iconic Turing Test in 1950 primarily to assess which capability of a machine?
A.Whether a machine can exhibit intelligent behavior indistinguishable from a human in natural text conversation
B.Whether a machine can compute floating-point mathematical equations without rounding errors
C.Whether a machine can physically move object blocks using robotic manipulators
D.Whether a machine can render 3D computer graphics in real-time
Explanation: The Turing Test evaluates if a human evaluator, conversing via text with an unseen machine and human, cannot reliably tell the machine apart from the human, demonstrating human-like conversational intelligence.
5An application predicting future stock prices based on historical financial tables belongs primarily to which AI domain?
A.Data Domain
B.Computer Vision Domain
C.Natural Language Processing Domain
D.Robotics Domain
Explanation: Financial numerical series and tabular historical prices represent structured numerical datasets, making stock prediction a classic Data Domain application.
6Which application is a direct implementation of the Computer Vision (CV) domain?
A.Automated facial recognition unlocking a smartphone
B.Spam email detection filtering incoming messages
C.Predicting monthly electricity consumption from past bill records
D.Language translation of an English article into Assamese
Explanation: Facial recognition requires capturing, processing, analyzing, and identifying facial features from digital camera imagery, which is a core Computer Vision task.
7Sentiment analysis of customer reviews on an e-commerce platform relies on which AI domain?
A.Natural Language Processing (NLP)
B.Computer Vision (CV)
C.Data Domain exclusively
D.Spatial Mapping Domain
Explanation: Sentiment analysis interprets human written text (reviews) to determine positive, negative, or neutral emotion, which is a key NLP task.
8How does a rule-based AI system differ fundamentally from a learning-based AI system?
A.Rule-based AI relies on explicit IF-THEN rules crafted by human experts, whereas learning-based AI learns patterns directly from data
B.Rule-based AI uses neural networks, while learning-based AI uses hardcoded lookup tables
C.Rule-based AI cannot run on modern computers, whereas learning-based AI requires quantum hardware
D.Rule-based AI requires large labelled training datasets, whereas learning-based AI requires no data
Explanation: Rule-based AI operates on hand-crafted logical rules (IF condition THEN action) programmed by domain experts, whereas learning-based AI (like ML) extracts patterns autonomously from training datasets.
9Smart home voice assistants such as Amazon Alexa or Google Assistant combine which AI domains to operate effectively?
A.Natural Language Processing (NLP) for speech recognition and intent understanding with Data Domain for information retrieval
B.Computer Vision exclusively for camera feed analysis
C.Rule-based mechanical actuation without data processing
D.Computer Vision and Hardware Robotics only
Explanation: Voice assistants convert spoken audio to text, process semantic intent using NLP, and query databases (Data Domain) to fetch responses and answer queries.
10Which statement accurately describes Narrow AI (Weak AI) compared to General AI (Strong AI)?
A.Narrow AI is designed to perform a specific dedicated task, whereas General AI possesses human-like generalized cognitive abilities across diverse domains
B.Narrow AI operates without electricity, whereas General AI uses supercomputers
C.Narrow AI refers to AI models with fewer than 10 lines of code, whereas General AI has over 1 million lines
D.Narrow AI can feel human emotions, whereas General AI cannot
Explanation: All current AI applications (such as chess bots, spam filters, facial recognition) are Narrow AI specialized for specific tasks. General AI (AGI) remains a theoretical concept capable of flexible human-level reasoning across any domain.

About the Assam HS Skill Subject Artificial Intelligence Exam

The Assam State School Education Board (ASSEB) Higher Secondary Skill Subject Artificial Intelligence curriculum introduces students to core AI domains (Computer Vision, Natural Language Processing, Data), Machine Learning concepts, Python programming with NumPy and Pandas, CV and NLP pipeline workflows, and ethical frameworks alongside employability skills.

Assessment

The ASSEB HS Skill AI examination includes a 50-mark theory paper (2 hours) and a 50-mark practical/project assessment, covering Class 11 and 12 NSQF Artificial Intelligence syllabus modules.

Time Limit

2 hours

Passing Score

30% aggregate in theory and practicals

Exam Fee

Standard ASSEB board examination fee (Assam State School Education Board (ASSEB))

Assam HS Skill Subject Artificial Intelligence Exam Content Outline

20%

Introduction to AI & AI Domains

Definition of AI, historical milestones, AI vs ML vs Deep Learning, smart technology applications, and core AI domains: Data, Computer Vision (CV), and Natural Language Processing (NLP).

20%

Machine Learning Basics & Model Evaluation

Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning), classification vs regression, clustering, training/testing splits, confusion matrix, accuracy, precision, recall, and F1-score.

20%

Python for AI & Data Science Libraries

Python syntax for data manipulation, NumPy arrays (creation, indexing, slicing, broadcasting), Pandas Series and DataFrames, data cleaning, aggregation, and basic data visualization.

20%

Computer Vision & NLP Pipelines

Image representation (pixels, RGB, grayscale), image processing, segmentation, feature extraction, text processing (tokenization, stop words, stemming, lemmatization, Bag of Words, TF-IDF), and pipeline workflows.

20%

AI Ethics, Bias & Employability Skills

Algorithmic bias, data privacy, ethical AI frameworks, AI impact on society and jobs, along with NSQF employability skills (communication, self-management, ICT skills, entrepreneurship).

How to Pass the Assam HS Skill Subject Artificial Intelligence Exam

What You Need to Know

  • Passing score: 30% aggregate in theory and practicals
  • Assessment: The ASSEB HS Skill AI examination includes a 50-mark theory paper (2 hours) and a 50-mark practical/project assessment, covering Class 11 and 12 NSQF Artificial Intelligence syllabus modules.
  • Time limit: 2 hours
  • Exam fee: Standard ASSEB board examination fee

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Assam HS Skill Subject Artificial Intelligence Study Tips from Top Performers

1Practice creating and manipulating 1D and 2D arrays using NumPy (array creation, slicing, reshaping, element-wise operations).
2Master Pandas DataFrames, including data loading, indexing with loc/iloc, handling missing values, and column filtering.
3Understand model evaluation metrics thoroughly: calculate confusion matrix metrics (Accuracy, Precision, Recall, F1-score) by hand.
4Memorize the stages of CV (acquisition, pre-processing, segmentation, feature extraction) and NLP (tokenization, stop words, stemming, lemmatization, TF-IDF) pipelines.
5Review AI ethics concepts such as algorithmic bias, data privacy (GDPR principles), and NSQF employability modules (communication, self-management, workplace safety).

Frequently Asked Questions

What is the examination pattern for ASSEB HS Skill Subject Artificial Intelligence?

The exam consists of a 50-mark theory paper (2 hours) featuring MCQs and short-answer questions, alongside a 50-mark practical examination involving Python coding and project portfolio presentation.

What core topics are covered under the Class 11 & 12 NSQF AI syllabus?

The syllabus covers Introduction to AI & AI Domains, Machine Learning & Model Evaluation, Python Data Science Libraries (NumPy and Pandas), Computer Vision & NLP Pipelines, and AI Ethics & Employability Skills.

What Python programming skills are required for the practical component?

Students need proficiency in Python data handling using NumPy arrays, Pandas DataFrames, basic data filtering, missing value handling, and simple data processing pipelines.

How are Machine Learning model evaluation metrics calculated?

Evaluation is based on confusion matrix metrics including Accuracy ((TP+TN)/Total), Precision (TP/(TP+FP)), Recall (TP/(TP+FN)), and F1-score (harmonic mean of Precision and Recall).

Are NSQF Employability Skills included in the examination?

Yes, employability skill modules such as communication skills, self-management, green skills, ICT literacy, and basic entrepreneurship form part of the curriculum.