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Gotowe quizy z pytaniami zamkniętymi, sprawdzone ręcznie. Rozwiąż dowolny darmowy quiz online — każda odpowiedź ma wyjaśnienie, a każdy quiz pełny klucz odpowiedzi do powtórki.
Wczytywanie quizów
Za darmo · bez rejestracji · bez limitu czasu
Gotowe quizy z pytaniami zamkniętymi, sprawdzone ręcznie. Rozwiąż dowolny darmowy quiz online — każda odpowiedź ma wyjaśnienie, a każdy quiz pełny klucz odpowiedzi do powtórki.
IT
Assess your understanding of practical machine learning architectures, embedding spaces, and Retrieval-Augmented Generation systems. Odpowiadaj na pytania i od razu sprawdzaj, czy to dobra odpowiedź — zawsze z wyjaśnieniem.
Darmowy test próbny z 16 pytaniami zamkniętymi z kategorii IT. Każde pytanie ma jedną poprawną odpowiedź i krótkie wyjaśnienie, a pełny klucz odpowiedzi pod quizem służy jako ściąga do nauki. Bez konta, bez limitu czasu — rozwiązuj, ile chcesz.
To darmowy quiz z 16 pytaniami zamkniętymi. Po każdej odpowiedzi zobaczysz, czy była poprawna, razem z wyjaśnieniem. Bez rejestracji i bez limitu czasu — rozwiązuj go, ile razy chcesz, a klucz odpowiedzi powyżej wykorzystaj do powtórki przed quizem albo po nim.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.
Poprawna odpowiedź: 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.