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10 sample questions
Deepseek MCQ Practice Test
It is a conversational AI assistant that understands and generates human-like text to help with a wide variety of tasks, including answering questions and drafting content.
Q1. What is the primary function of DeepSeek's attention mechanism in the context of multimodal fusion for AI applications?
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A. To reduce the dimensionality of input data
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B. To learn a hierarchical representation of the input data
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C. To combine information from different modalities ✓
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D. To optimize the model's parameters using gradient descent
Explanation: DeepSeek's attention mechanism allows the model to selectively focus on specific parts of the input data, weighing their importance in the decision-making process. This is particularly useful in multimodal fusion, where information from different sources (e.g., text, images, audio) needs to be integrated.
Q2. What is a key feature of DeepSeek, an open-source artificial intelligence framework, that allows it to efficiently process and analyze large datasets?
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A. Real-time data processing
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B. Distributed computing ✓
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C. Graph-based data structure
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D. Machine learning model interpretability
Explanation: DeepSeek utilizes distributed computing to process and analyze large datasets efficiently, enabling it to handle the computational demands of its large language models.
Q3. Which of the following is a key characteristic of DeepSeek models designed for edge devices?
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A. They are optimized for low-power consumption and efficient inference. ✓
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B. They utilize a large, pre-trained language model for image classification.
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C. They employ a hierarchical attention mechanism for improved accuracy.
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D. They are trained using a meta-learning approach.
Explanation: DeepSeek models are designed to be efficient and run on edge devices, which means they are optimized for low-power consumption and fast inference.
Q4. DeepSeek is a deep learning-based approach for "seeking" knowledge graph entities. Which of the following best describes the core function of DeepSeek in the context of entity disambiguation?
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A. DeepSeek focuses on identifying and ranking potential entity candidates based on contextual clues within a given text. ✓
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B. DeepSeek relies solely on entity recognition techniques to identify entities in unstructured data.
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C. DeepSeek leverages graph convolutional networks to propagate entity features across the knowledge graph.
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D. DeepSeek employs a rule-based approach to disambiguate entities by matching text patterns with known entity attributes.
Explanation: DeepSeek is designed to address the challenge of entity disambiguation by leveraging contextual clues within a given text to identify and rank potential entity candidates. This approach enables DeepSeek to effectively disambiguate entities in text data, especially in cases where entities are mentioned in a non-standard or ambiguous manner.
Q5. DeepSeek, a large language model, utilizes what technique to represent words and capture semantic relationships within text data?
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A. Latent Semantic Analysis (LSA)
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B. Latent Dirichlet Allocation (LDA)
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C. Term Frequency-Inverse Document Frequency (TF-IDF)
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D. Word Embeddings ✓
Explanation: DeepSeek leverages word embeddings, such as Word2Vec or GloVe, to represent words as dense vectors, capturing semantic relationships and enabling the model to understand the meaning and context of words within the text.
Q6. DeepSeek is an AI-powered search engine for scientific literature. Which of the following is a key feature of DeepSeek's natural language processing (NLP) component?
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A. It uses a supervised learning approach to improve its search results over time.
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B. It employs entity recognition to identify and extract relevant entities from scientific texts. ✓
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C. It relies on a rule-based approach to filter out irrelevant search results.
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D. It utilizes a hybrid approach combining both supervised and unsupervised learning techniques.
Explanation: DeepSeek's NLP component uses entity recognition to identify and extract relevant entities from scientific texts, allowing for more accurate and relevant search results.
Q7. In the context of DeepSeek, which of the following best describes the purpose of the "Entity Embedding" module?
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A. It is responsible for generating a knowledge graph of entities and their relationships.
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B. It is a component of the entity recognition module, tasked with identifying and categorizing entities within unstructured data.
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C. It is a neural network layer that converts text-based entity descriptions into numerical vectors, enabling efficient similarity searches. ✓
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D. It is a data preprocessing step that removes irrelevant information from the input dataset.
Explanation: The Entity Embedding module in DeepSeek is responsible for converting text-based entity descriptions into numerical vectors, allowing for efficient similarity searches and enabling the system to better understand the relationships between entities.
Q8. Which of the following is a key characteristic of the DeepSeek large language model?
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A. Use of graph convolutional networks for spatial reasoning
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B. Emphasis on efficient training with a mixture-of-experts architecture ✓
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C. Specialization in image classification tasks
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D. Reliance on recurrent neural networks for sequence processing
Explanation: DeepSeek is a large language model known for its efficient training, often employing a mixture-of-experts architecture to handle large datasets and complex tasks. This allows for scaling and improved performance.
Q9. What is DeepSeek best known for in the AI industry?
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A. Open-weight large language models such as DeepSeek-V3 and R1 ✓
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B. Underwater drones
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C. Video game consoles
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D. Solar panels
Explanation: DeepSeek is an AI company that has released open-weight LLMs; the original 'video analysis' framing was fabricated.
Q10. DeepSeek-R1, released in early 2025, is notable for what capability?
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A. Strong reasoning performance trained largely with reinforcement learning ✓
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B. Playing chess only
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C. Translating dead languages
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D. Predicting earthquakes
Explanation: DeepSeek-R1 is a reasoning model whose chain-of-thought ability was elicited primarily through reinforcement learning with verifiable rewards.
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