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| Section | Weight | Objectives |
|---|---|---|
| Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Software Development & Engineering | 15% | - Python libraries for multimodal AI - Integration and deployment of multimodal AI systems |
| Experimentation | 25% | - A/B testing - Model evaluation and comparison - Hypothesis testing - Experimental design |
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NEW QUESTION # 28
You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)
Answer: A,E
Explanation:
Precision and recall of ingredients directly assess whether the generated recipe includes the correct ingredients. Human evaluation provides a subjective assessment of the recipe's overall quality, clarity, and accuracy. BLEU score is a general text evaluation metric, but may not capture the specific requirements of recipe generation. Inception Score is relevant for image generation, not recipe generation. Cosine similarity of word embeddings can capture semantic similarity, but doesn't guarantee correctness of the recipe.
NEW QUESTION # 29
In the context of multimodal data analysis, which of the following statements accurately describe the challenges associated with data alignment?
Answer: B,D
Explanation:
Data alignment is crucial for ensuring that information from different modalities is correctly associated with the same event or entity. Misalignment can lead to incorrect relationships being learned by the model, resulting in poor performance. Data alignment is necessary for various types of multimodal data, not just time-series data. Perfect alignment is often difficult to achieve due to inherent noise and limitations in data collection. Deep learning models are also susceptible to issues caused by data misalignment.
NEW QUESTION # 30
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)
Answer: D,E
Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.
NEW QUESTION # 31
What is a main application of Triton Inference Server?
Answer: A
Explanation:
NVIDIA Triton Inference Server is an open-source model-serving platform designed to standardize production deployment across heterogeneous model formats and hardware backends. Its defining capability is multi-framework support: a single Triton instance can concurrently serve models trained in TensorFlow, PyTorch, ONNX Runtime, TensorRT, OpenVINO, and custom Python/C++ backends, exposing them through unified HTTP/REST and gRPC inference APIs. This eliminates the need for framework-specific serving stacks and lets teams standardize deployment infrastructure independent of how a given model was trained.
Option B is a common but incorrect assumption - Triton explicitly supports both GPU and CPU inference, which matters for cost-sensitive or edge deployments where GPU availability is limited. Option C confuses Triton with cuGraph, a separate RAPIDS library for GPU-accelerated graph analytics (unrelated to model serving), and option A describes a generative task (denoising diffusion), which is a *model capability*, not a Triton *server* function - Triton can host such a model, but "generating images from noise" is not what the server itself does.
Triton also provides dynamic batching, concurrent model execution, model ensembling (chaining pre/post- processing with inference), and metrics export - features tested elsewhere in the Software Development and Engineering and Performance Optimization domains.
Reference: Software Development and Engineering domain - Triton Inference Server, multi-framework model deployment.
NEW QUESTION # 32
Which metric is commonly used to evaluate machine-translation models?
Answer: C
Explanation:
BLEU (Bilingual Evaluation Understudy) is the standard automatic metric for evaluating machine translation quality. It measures n-gram precision - the overlap of contiguous word sequences (unigrams through typically 4-grams) between the model's translated output and one or more human reference translations - combined with a brevity penalty to discourage overly short translations that could otherwise achieve artificially high precision. BLEU scores range from 0 to 1 (or 0-100 as a percentage), with higher scores indicating closer alignment to reference translations.
The distractors represent metrics standard to other task families: F1 score (A) evaluates classification tasks by balancing precision and recall over discrete positive/negative predictions, ill-suited to open-ended text generation where there is no fixed set of "correct" tokens. Accuracy (B) similarly assumes a discrete correct
/incorrect judgment, inappropriate for translation where multiple valid phrasings can convey the same meaning. Mean Absolute Error (C) is a regression metric measuring average magnitude of numeric prediction error, irrelevant to text output evaluation entirely.
It's worth noting BLEU has known limitations - it correlates imperfectly with human judgments of fluency and can penalize valid paraphrases - which has motivated complementary metrics like METEOR, ROUGE (more common for summarization), and learned metrics like BERTScore, though BLEU remains the benchmark most commonly referenced for translation specifically.
Reference: Core Machine Learning and AI Knowledge / Multimodal Data domains - task-specific evaluation metrics (BLEU for translation, WER for ASR, MOS for TTS).
NEW QUESTION # 33
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