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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 2: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 3: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 4: Experimentation | 25% | - A/B testing - Experimental design - Hypothesis testing - Model evaluation and comparison |
| Topic 5: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 6: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 7: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
>> NCA-GENM Valid Exam Notes <<
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NEW QUESTION # 12
You are evaluating the performance of an AI model for facial recognition. What is an important consideration when evaluating the model for bias?
Answer: D
Explanation:
Bias evaluation for facial recognition centers on measuring whether the model's accuracy - true positive rate, false positive rate, false match rate - is consistent across demographic subgroups (race, gender, age), rather than assuming a single aggregate accuracy figure represents performance fairly for all groups. This concern is well grounded empirically: independent benchmarking, including NIST's Face Recognition Vendor Test studies, has repeatedly documented substantial accuracy disparities across demographic groups in widely deployed facial recognition systems, with error rates for some subgroups measured many times higher than for others - a direct consequence of training data underrepresentation and the representativeness-bias issue covered elsewhere in this domain.
Processing speed (A) is a performance/latency engineering concern, not a bias or fairness concern - a model could process all demographic groups at identical speed while still exhibiting severe accuracy disparities between them. Facial expression recognition capability (C) addresses a different task dimension (emotion
/expression classification) than the identity-recognition bias question being asked. Operating system compatibility (D) is a software-deployment/engineering concern entirely unrelated to model fairness.
Subgroup accuracy disparities in facial recognition carry serious real-world consequences - misidentification risk in law enforcement or access-control contexts - which is why disaggregated (per-subgroup) evaluation, not just aggregate accuracy, is considered a baseline requirement under Trustworthy AI bias auditing practice.
Reference: Trustworthy AI domain - subgroup/disaggregated bias evaluation, fairness auditing.
NEW QUESTION # 13
Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.
Answer: B
Explanation:
The correct code uses ' AutoModelForSeq2SeqLM' because BLIP (used in the example) is a sequence-to-sequence model. The processor correctly handles the image and text, and 'model.generate' produces the answer which is then decoded. 'AutoModelForQuestionAnswering' is not a generic class and won't work correctly with BLIP without additional adaptation.
NEW QUESTION # 14
You are building an A1 model that takes video and corresponding subtitles as input to generate short summaries of video content. Which of the following strategies are most important to reduce the chance of your model generating biased summaries? (Select all that apply)
Answer: B,C,E
Explanation:
Debiasing pre-trained models helps remove existing biases. A diverse training dataset is important to reduce the influence of any single biased viewpoint. Evaluating model performance on different demographic groups allows you to find and rectify performance disparities. Random data shuffling (D) and increasing training epochs (E) do not directly address bias. Note this is a very tough question as all choices seem viable but only options, A, B and C are the correct choice.
NEW QUESTION # 15
What is the role of CLIP (Contrastive Language-Image Pretraining) in text-to-image generation?
Answer: C
Explanation:
CLIP's core contribution to text-to-image pipelines is a shared, aligned embedding space in which semantically related text and images map to nearby vectors. In generative pipelines such as Stable Diffusion, CLIP's text encoder converts a prompt into an embedding that conditions the diffusion model's denoising process (often via cross-attention layers), steering the iterative noise-removal toward images whose CLIP embedding would be close to the prompt's embedding. In DALL-E 2's unCLIP approach, a "prior" model additionally maps text embeddings to plausible image embeddings within this same CLIP space before a decoder renders the final image.
Option B is subtly wrong: CLIP's text encoder produces a text embedding, not an "image embedding" - the point is that both modalities land in the *same* space, not that text is literally converted into an image representation. Option A confuses CLIP with an image-captioning model (a different task using an image encoder plus a text decoder, e.g., BLIP), and option D misattributes a data-augmentation role CLIP does not perform; CLIP is a representation/alignment model, not an augmentation tool.
Because CLIP was trained contrastively on hundreds of millions of image-text pairs, its embedding space also carries useful semantic structure (compositionality, style, attributes) that generative models exploit for prompt fidelity.
Reference: Multimodal Data domain - CLIP's role as a conditioning/guidance mechanism in generative pipelines.
NEW QUESTION # 16
You are working with a dataset containing text descriptions of products and corresponding product images. You want to train a model that can retrieve the most relevant image for a given text description. Which of the following loss functions is MOST appropriate for this task?
Answer: C
Explanation:
Triplet loss is specifically designed for learning embeddings where similar examples are close together in the embedding space and dissimilar examples are far apart. In this case, the triplets would consist of (text description, correct image, incorrect image). The goal is to learn embeddings that bring the text description and its corresponding image closer while pushing the text description and the incorrect image further apart. Cross-entropy and binary cross-entropy are typically used for classification tasks. MSE loss is used for regression tasks. Huber loss is for regression that's less sensitive to outliers. None of these are as well-suited as triplet loss for this retrieval task.
NEW QUESTION # 17
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