MCQs

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Overall MCQs for Reference

1. What does “multimodal learning” mean in AI?
   a. Learning from a single data type
   b. Integrating multiple data modalities
   c. Only processing numeric data
   d. Ignoring non-textual information
   Answer: b. Integrating multiple data modalitiesjoinmytutor​
2. Which common modalities might be integrated in multimodal AI models?
   a. Text and audio
   b. Images and video
   c. Text, image, audio
   d. Only images
   Answer: c. Text, image, audiojoinmytutor​
3. Which model is renowned for learning joint text-image embeddings?
   a. GPT-2
   b. Word2Vec
   c. CLIP
   d. BERT
   Answer: c. CLIPjoinmytutor​
4. The challenge of synchronizing multiple data types for effective processing is called:
   a. Data normalization
   b. Data augmentation
   c. Data alignment
   d. Data minimization
   Answer: c. Data alignmentjoinmytutor​
5. Fusion strategies in multimodal models include:
   a. Early fusion
   b. Late fusion
   c. Hybrid fusion
   d. All of the above
   Answer: d. All of the abovejoinmytutor​
6. Which is NOT a benefit of multimodal models?
   a. Increased robustness
   b. Lower computation cost
   c. Richer feature representation
   d. Improved accuracy in perception tasks
   Answer: b. Lower computation costieeexplore.ieee+1​
7. Visual Question Answering (VQA) relies on:
   a. Only images
   b. Only text
   c. Combining image and text modalities
   d. Only audio
   Answer: c. Combining image and text modalitiesjoinmytutor​
8. Image captioning tasks use:
   a. Only images
   b. Images and text
   c. Only text
   d. Audio and images
   Answer: b. Images and textjoinmytutor​
9. Which is a multimodal generative AI task?
   a. Text summarization
   b. Speech recognition
   c. Image captioning
   d. Data encryption
   Answer: c. Image captioninggyansetu+1​
10. Audio-visual scene understanding combines:
    a. Image and audio data
    b. Only video
    c. Only text
    d. Only audio
    Answer: a. Image and audio datajoinmytutor​
11. The main drawback of multimodal models is:
    a. Poor generalization
    b. Complexity in aligning modalities
    c. No practical applications
    d. Limited scope
    Answer: b. Complexity in aligning modalitiesjoinmytutor​
12. Multimodal learning in deep neural networks enhances:
    a. Single domain performance
    b. Cross-modal understanding
    c. Data redundancy
    d. Only visualization
    Answer: b. Cross-modal understandingjoinmytutor​
13. CLIP was released by:
    a. Google
    b. OpenAI
    c. Meta
    d. IBM
    Answer: b. OpenAIjoinmytutor​
14. Transformers support multimodal inputs via:
    a. Universal tokenization
    b. Modality-specific encoders
    c. Random embedding
    d. Max pooling
    Answer: b. Modality-specific encodersjoinmytutor​
15. Which fusion approach processes modalities together from the start?
    a. Early fusion
    b. Late fusion
    c. Recursive fusion
    d. Ensemble fusion
    Answer: a. Early fusionjoinmytutor​
16. Late fusion in multimodal models means:
    a. Fusing at input layer
    b. Combining features at output
    c. Ignoring one modality
    d. Reducing number of modalities
    Answer: b. Combining features at outputjoinmytutor​
17. Hybrid fusion combines:
    a. Only images
    b. Both early and late fusion
    c. No modalities
    d. Maximum pooling
    Answer: b. Both early and late fusionjoinmytutor​
18. Which is a use case of multimodal medical AI?
    a. Predicting diseases using text
    b. Radiology image analysis with patient records
    c. Only genetic data
    d. Speech-to-text
    Answer: b. Radiology image analysis with patient recordsyoutube​
19. Multimodal AI can power which virtual assistant feature?
    a. Text-based reply only
    b. Speech and image response
    c. Only audio
    d. Only data retrieval
    Answer: b. Speech and image responsejoinmytutor​
20. A multimodal large language model (MLLM) is designed to:
    a. Work only on text
    b. Process multiple types of data simultaneously
    c. Only generate images
    d. Compress images
    Answer: b. Process multiple types of data simultaneouslyqmro.qmul+1​
21. Hanooman is an example of:
    a. Unimodal AI
    b. Multimodal AI tool
    c. Classic ML algorithm
    d. Data warehouse
    Answer: b. Multimodal AI tooltestbook​
22. Deep learning models integrate multimodal data via:
    a. Feature concatenation
    b. Data shuffling
    c. Loss minimization
    d. Output normalization
    Answer: a. Feature concatenationjoinmytutor​
23. Why is data alignment critical for multimodal learning?
    a. Ensures synchronized feature extraction
    b. Reduces model parameters
    c. Maximizes modality separation
    d. Improves computation speed only
    Answer: a. Ensures synchronized feature extractionjoinmytutor​
24. Which type of task do multimodal models outperform unimodal models?
    a. Tasks with multiple data types
    b. Only text-based tasks
    c. Only numeric processing
    d. Data encryption
    Answer: a. Tasks with multiple data typesjoinmytutor​
25. Which AI field benefits most from multimodal approaches?
    a. Data encryption
    b. Computer vision
    c. Language translation
    d. Multimodal perception tasks
    Answer: d. Multimodal perception tasksjoinmytutor​
26. A multimodal model for self-driving cars typically processes:
    a. Only camera feeds
    b. Multiple sensors (camera, lidar, radar)
    c. Only audio signals
    d. Text annotations
    Answer: b. Multiple sensors (camera, lidar, radar)joinmytutor​
27. One example of cross-modal retrieval is:
    a. Retrieving images using text queries
    b. Generating audio from video
    c. Text summarization
    d. Encrypting data
    Answer: a. Retrieving images using text queriesjoinmytutor​
28. Multimodal learning is essential for:
    a. Generalizing across sensory inputs
    b. Reducing data volume
    c. Limiting model capability
    d. Only data clustering
    Answer: a. Generalizing across sensory inputsjoinmytutor​
29. What does multimodal sentiment analysis require?
    a. Only textual analysis
    b. Image and audio analysis
    c. Combining text, audio, and visual cues
    d. Single modality analysis
    Answer: c. Combining text, audio, and visual cuesjoinmytutor​
30. Which is a limitation of multimodal modeling?
    a. Computational overhead
    b. Universality
    c. No practical value
    d. Single output
    Answer: a. Computational overheadjoinmytutor​
31. Transfer learning for multimodal models involves:
    a. Reusing features across modalities
    b. Training only on single modality
    c. Ignoring pretrained weights
    d. Serializing outputs
    Answer: a. Reusing features across modalitiesjoinmytutor​
32. Which model architecture excels in multimodal fusion tasks?
    a. Decision tree
    b. CNN-RNN hybrid
    c. Simple regression
    d. Only SVM
    Answer: b. CNN-RNN hybridjoinmytutor​
33. Attention mechanism helps multimodal transformers by:
    a. Ignoring cross-modal links
    b. Focusing on relevant features across modalities
    c. Only normalizing output
    d. Minimizing data volume
    Answer: b. Focusing on relevant features across modalitiesjoinmytutor​
34. Application of multimodal AI in security includes:
    a. Text classification
    b. Facial and voice recognition together
    c. Only password checking
    d. Only CCTV monitoring
    Answer: b. Facial and voice recognition togetherjoinmytutor​
35. Modality-specific encoders do what in multimodal models?
    a. Encode each modality separately
    b. Combine all data directly
    c. Skip feature extraction
    d. Only output images
    Answer: a. Encode each modality separatelyjoinmytutor​
36. When modalities conflict, what does the model need?
    a. Data pruning
    b. Modality reliability weighting
    c. Removing all outputs
    d. No action needed
    Answer: b. Modality reliability weightingjoinmytutor​
37. Data augmentation in multimodal datasets helps to:
    a. Increase model robustness
    b. Decrease diversity
    c. Remove missing data
    d. Only works for images
    Answer: a. Increase model robustnessjoinmytutor​
38. Multimodal machine translation uses:
    a. Only text
    b. Images with text
    c. Audio only
    d. Numeric data only
    Answer: b. Images with textjoinmytutor​
39. Typical audio modality feature:
    a. MFCCs
    b. Token IDs
    c. RGB values
    d. Bounding boxes
    Answer: a. MFCCsjoinmytutor​
40. In late fusion, models:
    a. Train jointly from input
    b. Fuse outputs after unimodal processing
    c. Discard some modalities
    d. Operate unimodally only
    Answer: b. Fuse outputs after unimodal processingjoinmytutor​
41. Key metric for evaluating image-captioning models:
    a. BLEU score
    b. Perplexity
    c. AUC
    d. Recall only
    Answer: a. BLEU scoreinterview.quicktechie​
42. Which challenge is unique to multimodal sentiment analysis?
    a. Multimodal signal synchronization
    b. Text tokenization
    c. Audio noise reduction only
    d. Visual color balancing
    Answer: a. Multimodal signal synchronizationjoinmytutor​
43. End-to-end multimodal models perform:
    a. Task-specific differential processing
    b. Joint optimization on all data modalities
    c. Only pre-processing
    d. Output normalization only
    Answer: b. Joint optimization on all data modalitiesjoinmytutor​
44. Self-supervised learning enables multimodal models to:
    a. Learn cross-modal representations without labeled data
    b. Only use labeled data
    c. Skip training phase
    d. Always require human feedback
    Answer: a. Learn cross-modal representations without labeled datajoinmytutor​
45. Visual grounding refers to:
    a. Aligning objects in images with text descriptions
    b. Only object detection
    c. Data normalization
    d. Sentence tokenization
    Answer: a. Aligning objects in images with text descriptionsjoinmytutor​
46. Which is NOT a multimodal AI application?
    a. Object detection in images
    b. Multimodal emotion recognition
    c. Cross-modal retrieval
    d. Image captioning
    Answer: a. Object detection in imagesjoinmytutor​
47. Why do multimodal models need common embedding spaces?
    a. To enable cross-modal similarity search
    b. To separate modal outputs
    c. For hardware efficiency only
    d. To discard noise
    Answer: a. To enable cross-modal similarity searchjoinmytutor​
48. Multimodal models can fail due to:
    a. Modal imbalance
    b. Data redundancy
    c. High training speed
    d. No practical issues
    Answer: a. Modal imbalancejoinmytutor​
49. What is a “multimodal prompt” in generative AI?
    a. Only text input
    b. Combined input for text, image, audio
    c. Audio input only
    d. Numeric vector input
    Answer: b. Combined input for text, image, audioieeexplore.ieee+1​
50. The most suitable architecture for video captioning in multimodal AI is:
    a. CNN-LSTM
    b. Decision tree
    c. KNN
    d. Only RNN
    Answer: a. CNN-LSTMjoinmytutor​

1. https://joinmytutor.com/mcq/multi-modal-learning.php
2. https://ieeexplore.ieee.org/document/11016449/
3. https://www.gyansetu.in/blog/70-generative-ai-mcq-questions-and-answers-gyansetu/
4. https://www.youtube.com/watch?v=qhj5VIUZrlc
5. https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/107960/Shu%20Ai-Assisted%20Multiple-Choice%202025%20Accepted.pdf?sequence=2
6. https://testbook.com/question-answer/a-multimodal-ai-tool-that-can-respond-in-11i–67b484270247f281db1cc034
7. https://interview.quicktechie.com/certifications/practice-paper/14/55/NVIDIA/NVIDIA-Certified-Associate-Generative-AI-Multimodal–Exam-NCA-GENM/Practice-Paper-4
8. https://www.freecram.com/NVIDIA-certification/NCA-GENM-exam-questions.html
9. https://www.sanfoundry.com/artificial-intelligence-questions-answers/
10. https://testbook.com/objective-questions/mcq-on-artificial-intelligence–5eea6a0d39140f30f369e30e
11. https://github.com/youssefHosni/Data-Science-Interview-Questions-Answers/blob/main/Deep%20Learning%20Questions%20&%20Answers%20for%20Data%20Scientists.md
12. https://learninglabb.com/deep-learning-interview-questions-for-freshers/
13. https://www.igmguru.com/blog/artificial-intelligence-mcqs
14. https://flashgenius.net/blog-article/nca-genm-multimodal-data-practice-questions
15. https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-interview-questions
16. https://www.interviewbit.com/artificial-intelligence-mcq/
17. https://www.geeksforgeeks.org/deep-learning/deep-learning-interview-questions/
18. https://www.scribd.com/document/896012269/Chapter-2-MCQs-ClassXAI
19. https://devinterview.io/questions/machine-learning-and-data-science/deep-learning-interview-questions/
20. https://www.placementpreparation.io/mcq/artificial-intelligence/