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Ready-made multiple-choice practice quizzes, hand-checked. Take any free quiz online — every answer comes with an explanation, and every quiz page has a full answer key to revise from.
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Free · no sign-up · no time limit
Ready-made multiple-choice practice quizzes, hand-checked. Take any free quiz online — every answer comes with an explanation, and every quiz page has a full answer key to revise from.
IT
Assess your understanding of practical machine learning architectures, embedding spaces, and Retrieval-Augmented Generation systems. Answer each question to see whether you got it right, with an explanation every time.
A free 16-question multiple-choice practice test on IT. Each question has one correct answer and a short explanation, and the full answer key below the player doubles as a study sheet. No account, no time limit — take it as often as you like.
This is a free, 16-question multiple-choice quiz. Answer each question to see whether you got it right, with an explanation for every answer. There is no sign-up and no time limit — take it as many times as you like, and use the answer key above to revise before or after.
Correct answer: BTo ground LLM generation with external dynamic context fetched at inference time
RAG pulls relevant text chunks from external document indexes or databases and injects them into the prompt, mitigating hallucinations and stale training data.
Correct answer: ACosine similarity focuses on directional orientation rather than vector magnitude
Cosine similarity measures the angle between vectors, capturing semantic orientation independently of magnitude (text length), making it ideal for normalized dense embeddings.
Correct answer: BModels retrieve and utilize information located at the beginning and end of long prompts better than the middle
Empirical research demonstrates that transformer attention mechanisms frequently prioritize tokens at the extreme beginning and end of extensive contexts, degrading recall for middle context.
Correct answer: BTo jointly score query-document pairs with full self-attention to refine initial top-k retrieval results
While bi-encoders calculate query and document embeddings separately for fast retrieval, a cross-encoder examines query and document tokens together, yielding superior relevance rankings.
Correct answer: AIt generates a synthetic answer using an LLM, embeds that answer, and searches for similar real documents
HyDE prompts an LLM to generate a hypothetical answer to the user's question, then uses the embedding of that hypothetical text to find semantically aligned real source documents.
Correct answer: BLogarithmic search time complexity for Approximate Nearest Neighbor (ANN) vector queries
HNSW builds multi-layered graphs with skip-list properties, enabling fast logarithmic Approximate Nearest Neighbor (ANN) search through high-dimensional vector spaces.
Correct answer: BCrucial semantic context surrounding facts is severed, leading to incomplete or fragmented answers
Overly aggressive chunking isolates sentences from their surrounding context, meaning retrieved fragments may lack the pronouns, qualifications, or framing needed for accurate synthesis.
Correct answer: BIt splits documents at points where the semantic similarity between consecutive sentences drops sharply
Semantic chunking monitors embedding distance between adjacent sentences and introduces breaks when semantic coherence shifts significantly, keeping topical units intact.
Correct answer: AContrastive language-image pre-training (like CLIP)
Models like CLIP use contrastive learning to push matching text-image pairs close together in the shared embedding space while pulling non-matching pairs apart.
Correct answer: BFaithfulness (Groundedness)
In RAG evaluation frameworks (such as Ragas), Faithfulness specifically quantifies whether every claim made in the output can be directly inferred from the retrieved context.
Correct answer: BExtracting only the sentences directly relevant to the query from retrieved passages to conserve prompt tokens
Contextual compression inspects retrieved documents and strips out irrelevant filler text before passing the condensed, highly relevant content to the generation model.
Correct answer: BIt handles exact keyword matches (product codes, names) alongside broad conceptual semantic queries
Dense vector search excels at high-level semantic meaning but can miss exact alphanumeric matches (part numbers, acronyms), which sparse BM25 keyword search handles reliably.
Correct answer: BTo supply structured JSON schemas that instruct the model how to emit arguments to execute external functions
Tool schemas describe functions, parameter types, and descriptions in JSON format, allowing the LLM to output structured JSON matching the schema when an external action is required.
Correct answer: BFine-tuned models struggle to cite exact sources and suffer from knowledge obsolescence as facts change
Fine-tuning updates internal weights, making factual verification difficult, hallucination harder to trace, and keeping data up-to-date costly compared to updating an external RAG index.
Correct answer: BIt reduces model memory footprint and inference latency with minimal degradation in accuracy
Quantization maps high-precision floating-point weights (FP16/FP32) to lower-bit representations (INT8/INT4), drastically reducing VRAM usage and accelerating memory-bound inference.
Correct answer: BIt merges multiple ranked lists of search results into a single list based on position rather than raw scores
RRF combines rankings from disparate retrieval algorithms (e.g., keyword and vector) by scoring documents based on their inverse rank position, smoothing out incompatible score distributions.