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| Section | Weight | Objectives |
|---|---|---|
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Data Analysis & Visualization | 10% | - Data preprocessing and feature engineering - Visualization techniques for multimodal data |
| Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| Experimentation | 25% | - Model evaluation and comparison - Hypothesis testing - A/B testing - Experimental design |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
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NEW QUESTION # 45
Which of the following techniques is MOST suitable for aligning the feature spaces of text and images in a multimodal model?
Answer: B
Explanation:
Contrastive loss functions are designed to bring together the representations of similar data points (e.g., a picture and its caption) while pushing apart representations of dissimilar data points. This effectively aligns the feature spaces.
NEW QUESTION # 46
Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?
Answer: B
Explanation:
A box plot (box-and-whisker plot) summarizes the distribution of a numeric variable - median, interquartile range, and outliers - as a single compact glyph, and critically, multiple box plots can be placed side by side to compare distributions across categorical groupings. This makes it well suited to the scenario described:
comparing the spread and central tendency of performance scores across several models, further faceted by modality, in one readable figure. Box plots make skew, variance, and outlier prevalence immediately comparable across groups in a way a single summary statistic (like mean accuracy) cannot.
A histogram (B) shows the distribution of a single variable well but does not scale cleanly to side-by-side comparison across many model/modality combinations without becoming visually cluttered. A heatmap (A) is excellent for showing a matrix of values (e.g., mean score per model ร modality pair) but represents point estimates, not distributions - it cannot convey variance or spread. A pie chart (D) is inappropriate for any continuous performance metric.
In practice, a violin plot - which overlays a kernel density estimate on the box plot's summary statistics - is often preferred when the underlying distribution's shape (e.g., bimodality) matters, but among the given options, the box plot is the correct choice for distributional comparison across groups.
Reference: Data Analysis and Visualization domain - comparative distribution visualization, box plots vs.
heatmaps.
NEW QUESTION # 47
Consider a scenario where you are developing a system for automatically generating product descriptions based on images and specifications. The system needs to generate diverse and creative descriptions. Which of the following techniques would be MOST helpful in achieving this?
Answer: E
Explanation:
Fine-tuning a pre-trained language model (option B) is the most effective approach. Pre-trained language models like GPT-3 have a strong understanding of language and can generate fluent and creative text. Fine-tuning on a dataset of product descriptions, conditioned on image features, allows the model to learn to generate descriptions that are both accurate and engaging. Other options are either too rigid (A, D) or require more training data and computational resources (C).
NEW QUESTION # 48
You are tasked with deploying a generative A1 model using NVIDIA Triton Inference Server. Which configuration parameter within Triton is MOST crucial for optimizing throughput and minimizing latency when serving a large number of concurrent requests?
Answer: A
Explanation:
The 'Instance Group Count' parameter in Triton determines how many instances of the model are loaded onto the GPU(s) and/or CPU(s). Increasing the number of instances (up to the hardware's capacity) allows Triton to handle more concurrent requests in parallel, thereby improving throughput and reducing latency. While batching and max queue size can also help, the instance count is the most fundamental for parallelism. The default model filename is irrelevent to performance and input data type is a requirement not a performance consideration.
NEW QUESTION # 49
What is a common method to reduce the computational cost of deep learning models during inference?
Answer: C
Explanation:
Pruning removes weights, neurons, or entire filters/channels that contribute minimally to model output - identified via magnitude-based criteria (removing near-zero weights), sensitivity analysis, or more sophisticated importance scoring - producing a smaller, sparser model that requires fewer computations and less memory at inference time while aiming to preserve accuracy through careful selection and, often, a fine- tuning step after pruning to recover any lost performance. Structured pruning (removing entire filters
/channels) yields hardware-friendly speedups on standard accelerators, while unstructured pruning (removing individual weights) achieves higher sparsity ratios but requires specialized sparse-computation hardware or libraries to realize actual speed gains.
The remaining options move in the wrong direction or address a different concern: adding more convolutional filters (B) increases model capacity and parameter count, which increases computational cost, the opposite of the stated goal. Increasing batch size (D) affects training throughput and memory usage per step but does not reduce the per-sample computational cost of inference - a larger batch does more total work, not less per inference call, and batch size at inference is often constrained by latency requirements rather than optimization goals. Option C's premise - selectively replacing activation functions with simpler ones in
"some neurons" - is not a standard or well-defined optimization technique; activation function choice is typically uniform within a layer and driven by training dynamics, not a piecemeal inference-cost lever.
Reference: Performance Optimization domain - pruning, alongside quantization and kernel fusion, as core inference-cost reduction techniques.
NEW QUESTION # 50
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