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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Experimentation | 25% | - Hypothesis testing - Model evaluation and comparison - Experimental design - A/B testing |
| Topic 2: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 3: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 4: Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Topic 5: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 6: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 7: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
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NEW QUESTION # 50
Which data augmentation techniques are MOST suitable for improving the robustness of a multimodal model that uses images and text?
Answer: D,E
Explanation:
Rotating images and back-translating text are effective as they introduce variations that the model might encounter in real-world scenarios. Adding Gaussian noise and randomly deleting words helps the model become more robust to noisy or incomplete data. Cropping and translation, while augmentation techniques, don't specifically target multimodal robustness as effectively. Changing resolution and font are less likely to generalize well.
NEW QUESTION # 51
Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?
Answer: A,D,E
Explanation:
ReLU avoids saturation like sigmoid, helping gradients flow. Skip connections provide alternative pathways for gradients. Batch normalization stabilizes learning and can help mitigate vanishing gradients. Increasing learning rate is unrelated, and sigmoid exacerbates the problem due to saturation.
NEW QUESTION # 52
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 # 53
Consider the following scenario: You are building a multimodal system for autonomous driving that uses both camera images and LiDAR data to perceive the environment. The LiDAR data is sparse and noisy, while the camera images are rich in visual details but can be affected by lighting conditions. Which of the following fusion strategies is MOST robust and effective for combining these two modalities?
Answer: E
Explanation:
Early fusion allows the model to learn complex relationships between the two modalities, leveraging the strengths of each modality while mitigating their weaknesses. Raw data fusion can be difficult to train. Late fusion might miss important cross-modal interactions. Discarding either modality reduces the information available to the system. Camera images are important even though they might be affected by lighting conditions.
NEW QUESTION # 54
What does 'kernel fusion' refer to in the context of AI model optimization?
Answer: B
Explanation:
In GPU computing, "kernel" refers to a compiled function launched on the GPU to execute a specific operation (e.g., a matrix multiplication or an activation function). Executing a sequence of such operations naively launches a separate kernel for each one, incurring per-launch overhead (kernel launch latency) and requiring intermediate results to be written to and read back from GPU global memory between each operation - both of which waste time and memory bandwidth relative to the actual compute being performed. Kernel fusion combines multiple sequential operations into a single compiled kernel, so intermediate results stay in fast on-chip registers or shared memory rather than round-tripping through global memory, and only one kernel launch is needed instead of several. This reduces both launch overhead and memory-bandwidth-bound latency, which is often the dominant bottleneck for smaller operations on modern GPUs. NVIDIA's TensorRT applies kernel fusion (alongside quantization and precision calibration) as one of its core inference-optimization techniques, commonly fusing operations like convolution + bias + activation into a single kernel.
Option A describes pruning, a distinct technique covered elsewhere in this domain - reducing parameter count, not combining kernel launches. Option C misapplies "kernel" in the CNN-filter sense rather than the GPU-execution sense the question is asking about, and layering more kernels would not describe fusion at all.
Option D conflates kernel functions (as in kernel methods for SVMs) with GPU kernels - an unrelated use of the same term.
Reference: Performance Optimization domain - kernel fusion, TensorRT, GPU execution optimization.
NEW QUESTION # 55
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