Accenture Skill Proficiency Test - Large Language Models - Dec 2025

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📘 Accenture Proficiency Test on LLMs

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Q1. Few-shot Learning with GPT-3

Question (Cleaned)

You are developing a large language model using GPT-3 and want to apply few-shot learning techniques. You have a limited dataset for a specific task and want the model to generalize well. Which approach would be most effective?

Options:
a) Train the model on the entire dataset, then fine-tune on a small subset
b) Provide examples of the task in the input and let the model generate
c) LLMs are unable to handle single tasks

✅ Correct Answer

✔ b) Provide examples of the task in the input and let the model generate

💡 Hint

• Few-shot learning = prompt engineering

• No retraining required

• You show the task via examples in the prompt

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Q2. Edge AI with LLMs

Question

You are using an LLM for an edge AI application requiring real-time object detection. Which approach is most efficient?

Options:
a) Cloud-based LLM processing
b) Use a complex LLM regardless of compute
c) Use an LLM optimized for edge devices balancing accuracy and efficiency
d) Store data and process later
e) Manual input-based LLM

✅ Correct Answer

✔ c) Use an LLM optimized for edge devices

💡 Hint

• Edge AI prioritizes low latency + low compute

• Cloud = latency bottleneck

• “Optimized” is the keyword

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Q3. Improving Fine-tuned LLM Performance (Select Two)

Question

You fine-tuned a pre-trained LLM but performance is poor. What steps improve it?

Options:
a) Gather more annotated Q&A data with better supervision
b) Change architecture to Mixture of Experts / Mixture of Tokens
c) Simplify the task definition
d) Smaller chunks reduce retrieval complexity
e) Smaller chunks improve generation accuracy

✅ Correct Answers

✔ a) Gather more annotated data
✔ c) Simplify the task definition

💡 Hint

• First fix data quality & task framing

• Architecture changes come later

• Accenture favors practical ML hygiene

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Q4. Chunking in RAG Systems

Question

Why do smaller chunks improve a RAG pipeline?

Correct Statements:
✔ Smaller chunks reduce retrieval complexity
✔ Smaller chunks improve generation accuracy

💡 Hint

• Retrieval works better with semantic focus

• Too large chunks dilute meaning

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Q5. Challenges of Local LLMs in Chatbots

Question

What is a potential challenge local LLMs face in long-term task planning?

Options:
a) Unable to adjust plans when errors occur
b) Unable to handle complex tasks
c) Unable to handle multiple queries
d) Unable to use task decomposition

✅ Correct Answer

✔ a) Unable to adjust plans when errors occur

💡 Hint

• Local models lack persistent memory & feedback loops

• Planning correction is the real limitation

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Q6. RAG Pipeline – Poor Semantic Representation

Question

Why might embeddings not represent semantic meaning correctly?

Options:
a) Encoder not trained on similar data
b) Text chunks too large
c) Encoder incompatible with RAG
d) Incorrect chunk splitting
e) Encoder not initialized

✅ Correct Answers

✔ a) Domain mismatch in training data
✔ b) Chunk size too large

💡 Hint

• Embeddings fail due to domain shift or context overflow

• Initialization issues are rare in practice

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Q7. Designing Advanced RAG – Chunking Decision

Question

Which is NOT a valid reason for splitting documents into smaller chunks?

Options:
a) Large chunks are harder to search
b) Small chunks won’t fit in context window
c) Smaller chunks improve indexing efficiency

✅ Correct Answer

✔ b) Small chunks won’t fit in context window

💡 Hint

• Smaller chunks fit better, not worse

• This is a classic reverse-logic trap

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Q8. Intent in Chatbots

Question

What is the purpose of an intent in a chatbot?

✅ Correct Answer

✔ To determine the user’s goal

💡 Hint

• Intent ≠ entity

• Intent answers “what does the user want?”

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Q9. Healthcare LLM Security

Question

Which strategy best ensures privacy and compliance for patient data?

Options:
a) Public API & public cloud
b) Layered security: encryption, access control, audits, private network
c) No security changes
d) Plaintext storage
e) Unverified 3rd party services

✅ Correct Answer

✔ b) Layered security approach

💡 Hint

• Healthcare = defense in depth

• Accenture loves encryption + audits + private infra

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Q10. Edge AI Programming Language

Question

Which language is commonly used for developing ML models in Edge AI?

Options:

• Java

• Python

• C++

• JavaScript

• Ruby

✅ Correct Answer

✔ Python

💡 Hint

• Python dominates ML tooling

• C++ is used for deployment, not modeling

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Q11. Customizing METEOR Scoring

Question

How do you customize METEOR’s scoring function?

✅ Correct Answer

✔ Modify tool configuration or run with command-line flags

💡 Hint

• METEOR supports custom weighting

• Not hardcoded, no paid version needed

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Q12. Bias Mitigation in LLMs

Question

First step when an LLM is found biased?

✅ Correct Answer

✔ Identify the source of bias

💡 Hint

• Diagnosis before correction

• Retraining comes later

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Q13. DeepEval Tool – Advanced Features

Question

Which statement is correct about DeepEval advanced usage?

✅ Correct Answer

✔ Configure advanced features in Python scripts

💡 Hint

• Tool-level configuration

• No paid version needed

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Tags: Interview Preparation,Generative AI,Large Language Models,