GenAI & LLM Engineering Objective Questions with Answers & Explanations

This GenAI & LLM engineering quiz contains carefully curated objective questions with correct answers and clear explanations. It is designed for developers and ML engineers to test your skills in building, optimizing, evaluating, and deploying LLM-powered applications.

Practice GenAI & LLM Engineering MCQs with Detailed Explanations

Answer at least 12 questions to submit.

1.

Which technique is most suitable to reduce hallucinations in LLM-based applications that answer from private documents?

Medium

Fine-tuning on public data

Prompt chaining only

Retrieval-Augmented Generation (RAG)

Increasing temperature

2.

What is the primary role of embeddings in GenAI systems?

Easy

Generate natural language text

Represent text as dense vectors for semantic search

Compress model weights

Control randomness in outputs

3.

Which factor most directly impacts LLM inference latency in production?

Medium

Prompt temperature

Model parameter count

Tokenizer type

Number of training epochs

4.

What is a key benefit of using quantization for LLM deployment?

Medium

Improves training accuracy

Reduces memory footprint and speeds up inference

Eliminates hallucinations

Increases context window

5.

Which decoding strategy is most likely to increase output diversity but also hallucinations?

Medium

Greedy decoding

Beam search

Low temperature sampling

High temperature sampling

6.

In a RAG pipeline, what is the main purpose of chunking documents?

Medium

Reduce model size

Improve embedding granularity for better retrieval

Encrypt documents

Train the LLM faster

7.

Which metric is most appropriate for evaluating retrieval quality in a vector search system?

Hard

BLEU

Recall@k

Perplexity

ROUGE-L

8.

What is the main advantage of parameter-efficient fine-tuning (PEFT) methods like LoRA?

Medium

Require full model retraining

Lower compute and memory cost

Increase model size

Eliminate the need for data

9.

Which approach best mitigates prompt injection attacks in production systems?

Hard

Allow raw user prompts

Strict output formatting only

Input sanitization and instruction hierarchy

Higher temperature

10.

What is the main purpose of a system prompt in LLM applications?

Easy

Store embeddings

Control model behavior and role

Reduce inference latency

Cache outputs

11.

Which architecture component enables LLMs to capture long-range dependencies?

Medium

Convolution layers

Self-attention mechanism

Pooling layers

Dropout layers

12.

What is the primary drawback of increasing context window size?

Medium

Lower accuracy

Higher compute and memory cost

Less expressive embeddings

Slower tokenization only

13.

Which technique helps prevent data leakage from training data in deployed LLMs?

Hard

Prompt templating

Differential privacy during training

High temperature sampling

Chunk overlap

14.

What is the key role of rerankers in a RAG pipeline?

Medium

Generate embeddings

Reorder retrieved documents by relevance

Fine-tune the LLM

Compress vectors

15.

Which failure mode occurs when the LLM answers confidently with incorrect facts?

Easy

Mode collapse

Hallucination

Catastrophic forgetting

Overfitting

Practice GenAI & LLM Engineering MCQs with Detailed Explanations

1.

Which technique is most suitable to reduce hallucinations in LLM-based applications that answer from private documents?

2.

What is the primary role of embeddings in GenAI systems?

3.

Which factor most directly impacts LLM inference latency in production?

4.

What is a key benefit of using quantization for LLM deployment?

5.

Which decoding strategy is most likely to increase output diversity but also hallucinations?

6.

In a RAG pipeline, what is the main purpose of chunking documents?

7.

Which metric is most appropriate for evaluating retrieval quality in a vector search system?

8.

What is the main advantage of parameter-efficient fine-tuning (PEFT) methods like LoRA?

9.

Which approach best mitigates prompt injection attacks in production systems?

10.

What is the main purpose of a system prompt in LLM applications?

11.

Which architecture component enables LLMs to capture long-range dependencies?

12.

What is the primary drawback of increasing context window size?

13.

Which technique helps prevent data leakage from training data in deployed LLMs?

14.

What is the key role of rerankers in a RAG pipeline?

15.

Which failure mode occurs when the LLM answers confidently with incorrect facts?

Other Objective Questions

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GenAI & LLM Engineering Objective Questions and Answers

Practice GenAI & LLM Engineering MCQs with Detailed Explanations

1.

Which technique is most suitable to reduce hallucinations in LLM-based applications that answer from private documents?

2.

What is the primary role of embeddings in GenAI systems?

3.

Which factor most directly impacts LLM inference latency in production?

4.

What is a key benefit of using quantization for LLM deployment?

5.

Which decoding strategy is most likely to increase output diversity but also hallucinations?

6.

In a RAG pipeline, what is the main purpose of chunking documents?

7.

Which metric is most appropriate for evaluating retrieval quality in a vector search system?

8.

What is the main advantage of parameter-efficient fine-tuning (PEFT) methods like LoRA?

9.

Which approach best mitigates prompt injection attacks in production systems?

10.

What is the main purpose of a system prompt in LLM applications?

11.

Which architecture component enables LLMs to capture long-range dependencies?

12.

What is the primary drawback of increasing context window size?

13.

Which technique helps prevent data leakage from training data in deployed LLMs?

14.

What is the key role of rerankers in a RAG pipeline?

15.

Which failure mode occurs when the LLM answers confidently with incorrect facts?

Other Objective Questions