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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 2: Performance Optimization | 10% | - Monitoring and improving system efficiency - Techniques for optimizing AI performance |
| Topic 3: Trustworthy AI | 5% | - Ensuring fairness and transparency - Ethical considerations in AI development |
| Topic 4: Core ML & AI Knowledge | 20% | - Key algorithms and techniques - Basic concepts and terminology |
| Topic 5: Multimodal Data | 15% | - Applications and use cases - Handling and integrating text, image, and audio data |
| 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 # 12
You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?
Answer: C
Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.
NEW QUESTION # 13
What is the correct order of steps in an ML project?
Answer: B
Explanation:
The standard ML project lifecycle proceeds: data collection first, since you need raw data before anything else can happen; data preprocessing next, to clean, transform, and prepare that raw data (handling missing values, normalization, encoding, splitting into train/validation/test sets) into a form a model can consume; model training next, where the algorithm learns patterns from the preprocessed training data; and model evaluation last, where the trained model's performance is measured on held-out data it did not see during training. Each stage depends on the output of the one before it - you cannot preprocess data you haven't collected, train on data that hasn't been cleaned and split, or evaluate a model that hasn't been trained - which is what makes B the only internally consistent ordering among the four options.
Options A, C, and D each place a downstream step before its prerequisite: A attempts preprocessing before collection (nothing to preprocess yet); C and D both place evaluation before training and, in D's case, before data even exists - evaluation requires a trained model to assess, so it cannot logically precede training or the data-collection/preprocessing steps that training itself depends on.
In practice this pipeline is iterative rather than strictly linear - evaluation results often send you back to preprocessing (feature engineering) or even data collection (targeted collection to address weak subgroups) - but the canonical forward sequence for a first pass remains collection # preprocessing # training # evaluation.
Reference: Core Machine Learning and AI Knowledge domain - the standard ML project/pipeline lifecycle.
NEW QUESTION # 14
You are building a Generative A1 application that processes images and text. The image data has missing pixel values, and the text data contains inconsistencies in abbreviations. Which data preprocessing techniques are MOST suitable to address these issues effectively?
Answer: A,B
Explanation:
KNN imputation is more robust than mean imputation for images as it considers neighboring pixels. Regular expressions and fuzzy matching provide more accurate abbreviation handling compared to simply removing or ignoring them. KNN imputation and Median imputations both can work well. Fuzzy Matching can also resolve ambiguities in abreviations
NEW QUESTION # 15
You are evaluating a generative A1 model for image captioning. The model produces captions that are grammatically correct but often miss key objects in the image. Which of the following evaluation metrics would be MOST suitable to identify this deficiency?
Answer: C
Explanation:
CIDEr is designed to evaluate image captions by comparing them to a set of reference captions, emphasizing consensus and relevance to the image content. It is more sensitive to the presence of key objects than BLEU or ROUGE, which primarily focus on n-gram overlap and grammatical correctness. Perplexity and Inception Score are not relevant for evaluating image captioning.
NEW QUESTION # 16
You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?
Answer: A
Explanation:
Attention heatmaps are a visualization technique used to highlight the regions of an image that the model is focusing on when processing specific words in the input text. By overlaying the attention weights onto the image, you can identify the most relevant areas. t-SNE and PCA are dimensionality reduction techniques used for visualizing high-dimensional data in lower dimensions. ROC curves and confusion matrices are used to evaluate the performance of classification models.
NEW QUESTION # 17
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