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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization | 10% | - Techniques for optimizing AI performance - Monitoring and improving system efficiency |
| Topic 2: Experimentation | 25% | - A/B testing - Hypothesis testing - Model evaluation and comparison - Experimental design |
| Topic 3: Multimodal Data | 15% | - Handling and integrating text, image, and audio data - Applications and use cases |
| Topic 4: Software Development & Engineering | 15% | - Integration and deployment of multimodal AI systems - Python libraries for multimodal AI |
| Topic 5: Data Analysis & Visualization | 10% | - Visualization techniques for multimodal data - Data preprocessing and feature engineering |
| Topic 6: Core ML & AI Knowledge | 20% | - Basic concepts and terminology - Key algorithms and techniques |
| Topic 7: Trustworthy AI | 5% | - Ethical considerations in AI development - Ensuring fairness and transparency |
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NEW QUESTION # 26
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?
Answer: A
Explanation:
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.
NEW QUESTION # 27
You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?
Answer: B
Explanation:
Reviewer note: Marked answer (D, pie chart) is inconsistent with standard data-visualization practice for year-by-year, multi-region comparison; a line chart (B) is the technically defensible choice.
I need to flag this one directly: the marked answer (D, pie chart) does not hold up technically, and I won't present it as correct just because it's what the answer key says. A pie chart shows the proportional breakdown of a whole at a single point in time - it has no mechanism for representing a trend across ten years, and using ten overlapping pie charts (one per year) to compare regional performance would be one of the least readable choices available, not the most appropriate.
The technically correct choice is a line chart (B): with ten years of data per region, a line chart plots each region as a separate series across a shared time axis, making year-over-year trends, growth rates, inflection points, and cross-region divergence immediately visible - exactly the "year-by-year" comparison the question specifies. A grouped/clustered bar chart (C) is a reasonable secondary choice if the emphasis is discrete year-to-year comparison rather than continuous trend, but it becomes visually cluttered with ten years
× multiple regions. A scatter plot (A) is better suited to examining the relationship between two continuous variables (e.g., sales vs. marketing spend) than to a time-series comparison across categories.
If this exact answer appears on a live exam or official material, treat D with skepticism - this explanation reflects standard data visualization practice, not the source document's marked key.
NEW QUESTION # 28
You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?
Answer: A
Explanation:
Increasing the audio branch's complexity while simplifying the image branch would actually exacerbate the modality bias towards audio. The other techniques (modality dropout, gradient blending, loss reweighting, and adversarial training) are all strategies designed to encourage the model to utilize both modalities more evenly. Increasing the model parameters of one mode leads to over-representation of that mode.
NEW QUESTION # 29
You're evaluating the performance of a video captioning model. The model generates captions for video clips. You notice that while the captions are generally accurate, they often lack detail and creativity. Which metric(s) would be MOST suitable for assessing the diversity and originality of the generated captions? (Select all that apply)
Answer: E
Explanation:
Self-BLEU calculates the BLEU score of a set of generated captions against themselves. A lower Self-BLEU score indicates higher diversity, as it means the captions are less similar to each other. The other metrics (BLEU, CIDEr, SPICE, ROUGE) primarily focus on accuracy and semantic similarity to reference captions, not diversity. Although, SPICE could capture the semantic similarity of different captions, Self-BLEU is most used.
NEW QUESTION # 30
Consider a scenario where you're training a generative A1 model to create realistic images from text descriptions. You notice that the generated images lack fine-grained details and appear blurry. Which of the following loss functions or training techniques could you employ to improve the image quality and sharpness?
Answer: C
Explanation:
Perceptual loss is specifically designed to capture high-level perceptual similarities between images, leading to sharper and more realistic outputs- MSE and L1 losses tend to produce blurry images as they penalize pixel-wise differences without considering perceptual similarity. Cross-entropy loss is relevant for classification tasks, not image generation directly from text descriptions. Increasing the batch size can improve training stability but does not directly address the lack of fine-grained details.
NEW QUESTION # 31
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