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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Experimentation | 25% | - Experimental design - A/B testing - Model evaluation and comparison - Hypothesis testing |
| Topic 2: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Topic 3: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 4: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 5: Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Topic 6: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 7: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
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NEW QUESTION # 51
You have been given a dataset with missing values. What is the first step you should take with the data?
Answer: A
Explanation:
Before deciding *how* to handle missing data, best practice requires understanding *why* it's missing - analyzing whether missingness is Missing Completely at Random (MCAR, no systematic pattern), Missing at Random (MAR, related to other observed variables but not the missing value itself), or Missing Not at Random (MNAR, related to the missing value itself, e.g., patients with severe symptoms being less likely to complete a survey field). This diagnostic step determines which downstream handling strategy is statistically appropriate: naive row deletion under MNAR conditions can introduce systematic bias into the remaining dataset, while mean/median imputation applied blindly can distort variance and correlational structure if missingness isn't actually random.
Options B, C, and D each jump directly to a specific remedial action without first establishing whether that action is appropriate for the missingness pattern present. Removing rows (B) sacrifices sample size and can bias results if missingness correlates with the outcome of interest. Filling with a default value (C) without understanding the pattern risks introducing artificial structure that doesn't reflect the true underlying data.
Removing entire columns (D) may discard genuinely informative features if missingness in that column is low or non-systematic.
Only after this initial pattern analysis should you select an appropriate strategy: listwise deletion, mean/median
/mode imputation, model-based imputation (e.g., MICE, k-NN imputation), or explicit missingness indicators as additional features.
Reference: Data Analysis and Visualization domain - missing data diagnosis (MCAR/MAR/MNAR) prior to imputation strategy selection.
NEW QUESTION # 52
Consider the following code snippet that uses the NVIDIA cuBLAS library. Which statement best describes the purpose and potential benefits of this code?
Answer: C
Explanation:
The code uses the 'cublasSgemm' function, which is part of the NVIDIA cuBLAS library. cuBLAS is a GPU-accelerated library for performing basic linear algebra subroutines, including matrix multiplication. Using cuBLAS can significantly speed up matrix multiplication compared to CPU-based implementations.
NEW QUESTION # 53
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)
Answer: D
Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.
NEW QUESTION # 54
Assume you need to implement a multimodal pipeline to diagnose brain cancer type using MRI scans and their corresponding radiology reports. What do you need to include in the ablation study?
Answer: D
Explanation:
An ablation study systematically removes or isolates individual components of a system to measure each one's individual contribution to overall performance. In a multimodal pipeline combining MRI scans and radiology reports, a proper ablation study requires training and evaluating separate unimodal pipelines - an image-only model on MRI scans alone, and a text-only model on radiology reports alone - alongside the full multimodal pipeline. Comparing these unimodal baselines against the combined system's performance is what actually demonstrates whether fusion is adding genuine diagnostic value beyond what either modality provides independently, and it surfaces whether one modality is doing most of the work while the other contributes marginally (or is even introducing noise) - critical information for both model design decisions and clinical validation in a high-stakes diagnostic context.
Option A describes an early-fusion design choice, not an ablation methodology - it's a modeling decision, not a validation technique for understanding component contribution. Option C proposes abandoning one modality's diagnostic value entirely, which undermines rather than tests the multimodal hypothesis. Option D describes data quality/preprocessing work relevant earlier in the pipeline, not the comparative, component- isolating structure that defines an ablation study.
In a clinical context specifically, this ablation approach is also essential for regulatory and interpretability purposes - demonstrating that a diagnostic claim rests on genuine cross-modal signal, not a spurious correlation from a single dominant input.
Reference: Multimodal Data / Experimentation domains - ablation studies for validating fusion architecture design.
NEW QUESTION # 55
You are tasked with building a multimodal A1 system that can generate video descriptions from video footage. You have experimented with several architectures, including combining CNNs for visual feature extraction and LSTMs for sequence generation. However, you are facing challenges with the model capturing long-range dependencies in the video. Which of the following architectural modifications or training techniques is MOST likely to address this issue?
Answer: B
Explanation:
Transformers are known for their ability to capture long-range dependencies due to their self-attention mechanism. Replacing LSTMs with Transformers allows the model to attend to relevant parts of the video sequence regardless of their temporal distance. While CNNs can extract visual features, they don't inherently address long-range dependencies. RNNs are prone to vanishing gradients, making it difficult to learn long- range dependencies. Reducing the frame rate or batch size doesn't directly address the issue of capturing long-range dependencies within the video sequence.
NEW QUESTION # 56
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