UPSC CSE Prelims
Machine Learning Previous Year Questions (PYQs)
Practice solved questions for Machine Learning with detailed step-by-step solutions, key insights, and trend analysis for UPSC CSE PRELIMS.
Solved Previous Year Questions
Filter & practice questions topic-wise
Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct ?
- LLMs assign probabilities to the next possible words and then pick the one with the highest probability.
- LLMs process data through mathematical optimization to minimise prediction errors.
- LLMs produce unbiased outputs.
Select the answer using the code given below :
Detailed Explanation:
Statement 1 — Correct. LLMs analyze input context and calculate a statistical probability distribution for the next possible word (token). In their basic form (greedy decoding), they pick the word with the highest probability. While real-world applications use controlled randomness (top-k sampling, temperature scaling) for creativity, the foundational mechanism is probability-based selection.
Statement 2 — Correct. LLM training relies on mathematical optimization:
- Algorithms like Stochastic Gradient Descent (SGD) and backpropagation iteratively adjust billions of model parameters
- The explicit goal is to minimize a "loss function" — a mathematical measure of prediction errors vs. actual training data
Statement 3 — Incorrect. LLMs do NOT produce unbiased outputs. Since they learn from massive human-generated datasets (internet text), they inherit, reflect, and sometimes amplify historical and societal biases — including gender, racial, and cultural stereotypes — present in training data.
Key Trick: Statement 3 uses an absolute term — "unbiased" — which is a classic UPSC red flag. No AI model trained on human data can be completely unbiased.
Consider the following statements:
I. It is expected that Majorana 1 chip will enable quantum computing.
II. Majorana 1 chip has been introduced by Amazon Web Services (AWS).
III. Deep learning is a subset of machine learning.
Which of the statements given above are correct?
Detailed Explanation:
The Majorana 1 chip is a breakthrough quantum computing chip developed by Microsoft and is expected to accelerate the development of fault-tolerant quantum computers.
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Statement I is Correct: Majorana 1 is designed to support the future of quantum computing.
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Statement II is Incorrect: The chip was introduced by Microsoft, not Amazon Web Services (AWS).
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Statement III is Correct: Deep Learning is a specialized subset of Machine Learning that uses multi-layered neural networks.
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Therefore, only Statements I and III are correct, making Option C the correct answer.
Statement-wise Analysis
| Statement | Status | Explanation |
|---|---|---|
| I. Majorana 1 chip will enable quantum computing | ✅ Correct | It is designed to support scalable and fault-tolerant quantum computers. |
| II. Introduced by AWS | ❌ Incorrect | Majorana 1 was developed by Microsoft. |
| III. Deep learning is a subset of machine learning | ✅ Correct | Deep learning is an advanced branch of machine learning using neural networks. |
Majorana 1 Chip
What is Majorana 1?
Majorana 1 is Microsoft's quantum computing chip based on topological qubits, which aim to reduce errors and improve quantum stability.
Why is it important?
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Helps build scalable quantum computers.
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Uses Majorana particles (theoretical quasiparticles) for more reliable qubits.
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Expected to solve complex problems beyond the capability of classical computers.
Potential Applications
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Drug discovery
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Material science
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Climate modelling
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Cryptography
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Artificial Intelligence
Machine Learning vs Deep Learning
| Machine Learning | Deep Learning |
|---|---|
| Broad field of AI | Subset of Machine Learning |
| Often requires manual feature selection | Learns features automatically |
| Works well with smaller datasets | Performs best with large datasets |
| Simpler models | Uses multi-layer neural networks |
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Common questions about Machine Learning in UPSC CSE PRELIMS