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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 2: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
| Topic 3: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 4: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 5: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 6: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 7: Experimentation | 25% | - A/B testing - Experimental design - Hypothesis testing - Model evaluation and comparison |
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NEW QUESTION # 22
When building a multimodal chatbot that handles both text and voice inputs, what are the primary challenges related to data alignment and synchronization that you need to address?
Answer: A
Explanation:
All the options mentioned are challenges related to data alignment and synchronization. The vocabulary of encoders, handling paraphrasing between text and voice, and dealing with variations in speech are critical aspects of building a robust multimodal chatbot.
NEW QUESTION # 23
You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)
Answer: B,D
Explanation:
Dropout prevents overfitting by randomly dropping neurons during training, forcing the network to learn more robust representations and reducing reliance on specific features. Label smoothing encourages the model to be less confident in its predictions, making it less likely to overfit to the training data and repeat phrases. The size of the vocabulary, the number of attention heads, and L1 regularization on embeddings are less directly related to preventing repetition.
NEW QUESTION # 24
You are training a Generative Adversarial Network (GAN) for image synthesis. The discriminator loss is consistently near zero while the generator loss fluctuates significantly. Which of the following is the most likely cause and the best approach to address it?
Answer: A
Explanation:
A discriminator loss near zero indicates it's easily distinguishing real from fake images. The fluctuating generator loss means it's struggling to fool the discriminator. This often signifies mode collapse, where the generator produces a limited variety of outputs. Techniques like mini-batch discrimination (allowing the discriminator to compare the diversity of generated samples) or spectral normalization (constraining the Lipschitz constant of the discriminator) can help prevent this.
NEW QUESTION # 25
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?
Answer: D
Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.
NEW QUESTION # 26
What is the significance of A/B testing in ML software engineering?
Answer: A
Explanation:
A/B testing in an ML engineering context is a controlled experimental methodology where two (or more) model variants - for example, a current production model (A) and a candidate replacement (B) - are deployed simultaneously to randomly assigned, statistically comparable segments of live traffic, and their real- world performance is compared on business or task-relevant metrics (conversion rate, click-through rate, task accuracy, latency-adjusted engagement). This provides causal evidence of which model performs better under actual production conditions, which offline evaluation on static held-out datasets cannot fully capture, since production data distributions shift and downstream user behavior interacts with model outputs in ways offline metrics miss.
Option A narrows A/B testing incorrectly to UI changes alone; while A/B testing originated in and remains common for UI/UX experimentation, its application in ML engineering explicitly extends to comparing model versions, algorithms, and feature sets - not just interface elements. Option B misattributes a distinct methodology (systematic hyperparameter search, covered in the previous question) to A/B testing, which evaluates already-trained model variants rather than searching a hyperparameter space. Option C is simply incorrect - A/B testing is a cornerstone of responsible ML deployment and MLOps practice, gating rollout decisions before full production release.
Reference: Experimentation domain - A/B testing, online evaluation, controlled experimentation in production ML.
NEW QUESTION # 27
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