NVIDIA Generative AI Multimodal NCA-GENM Exam Questions

Answer : D

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.

Answer : C

Data cleansing (or data cleaning) operates on data you already have: it identifies and resolves quality issues within an existing dataset --- handling missing values (A), removing duplicate records (D), correcting formatting or type inconsistencies (B), fixing structural errors, and standardizing units or encodings. Collecting additional data (C) belongs to a conceptually earlier and separate phase of the pipeline: data acquisition or data collection, which determines what data enters the pipeline in the first place, rather than what is done to improve the quality of data already collected.

This distinction matters operationally: a cleansing step is typically deterministic and reversible against the existing dataset (you can inspect, log, and audit exactly which rows were dropped or imputed), whereas collecting more data is a scoping decision that may require new labeling budgets, new consent/privacy review, or new data-source integration --- a materially different workflow with different stakeholders.

That said, insufficient data volume discovered *during* cleansing (e.g., after removing corrupted records the sample size becomes too small for the target class) can trigger a decision to go back and collect more --- but that action itself is not classified as a cleansing step; it is the trigger for restarting an earlier pipeline stage.

Answer : A

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.

Answer : A

In a denoising diffusion probabilistic model (DDPM), the U-Net serves as the noise-prediction network at the core of the iterative generation process: at each reverse-diffusion timestep, the U-Net takes the current noisy image (and typically a timestep embedding, plus conditioning information like a CLIP text embedding in text-to-image models) as input and predicts the noise component present at that step. Subtracting this predicted noise incrementally, over many timesteps starting from pure Gaussian noise, progressively denoises the input into a coherent image --- the mechanism by which DDPMs generate new images from pure noise. U-Net's architecture --- a contracting encoder path paired with an expanding decoder path, connected by skip connections at matching resolutions --- is well suited to this role because the skip connections preserve fine-grained spatial detail that would otherwise be lost through the network's downsampling bottleneck, which matters for producing sharp, high-fidelity denoised output at each step.

Options B, C, and D describe discriminative tasks --- classification, detection, and segmentation --- that describe *other* legitimate applications of U-Net-style architectures (originally developed for biomedical image segmentation) but do not describe its function specifically *within* the diffusion generative process. Within a DDPM pipeline specifically, U-Net's role is generative noise prediction supporting image synthesis from noise, not classification or detection of any kind.

Answer : A, C

Padding behaves in a genuinely counter-intuitive, and often confused, way between standard convolution and transposed convolution, which is exactly why this pairing is tested together. In standard convolution, adding padding to the input before the kernel slides across it effectively increases the input's spatial extent, which --- for a fixed kernel size and stride --- increases (or, in 'same' padding, preserves) the resulting output feature map's spatial dimensions relative to the unpadded case; padding this way also allows the kernel to be centered properly over boundary/edge pixels, which would otherwise be under-sampled compared to interior pixels (option C's first half).

In transposed convolution (sometimes called 'deconvolution,' used for upsampling in decoder/generator architectures), padding operates on the *output* side after the input has already been expanded by inserting stride-related spacing between elements: the padding parameter specifies how many rows/columns to *remove* from the perimeter of that expanded, computed output --- meaning padding in transposed convolution shrinks rather than grows the resulting output dimensions, the reverse of its effect in standard convolution. This gives option A's directional claim and option C's second half.

Option B reverses which operation padding applies to (input vs. output) for each case. Option D is incorrect --- padding's purpose is spatial-dimension and boundary handling, not memory/compute reduction (padding, if anything, typically adds slightly more computation). Option E states an artificial, non-standard usage rule that doesn't reflect how padding is actually applied in practice.

Answer : A

In the context described --- comparing the performance of different AI models against each other --- the purpose of evaluation is to systematically measure each candidate model's performance on relevant metrics (accuracy, F1, WER, BLEU, latency, or task-specific measures) using held-out data, in order to determine which architecture, configuration, or training approach performs best for the target task. This is the immediate, operational purpose of the evaluation experiment being described: comparative performance measurement that informs model-selection decisions.

The other options describe legitimate but distinct concerns that belong to different domains within a full AI development lifecycle rather than to the 'evaluate performance of different models' activity specifically described in the question: ethical implications

(B) fall under Trustworthy AI governance --- fairness audits, bias assessments, and impact reviews --- conducted alongside, not as a substitute for, performance evaluation. Studying impact on human behavior (C) belongs to human-computer interaction or longitudinal deployment studies, a separate research activity from a controlled model-comparison experiment. Cost-effectiveness analysis (D) is a business/engineering consideration weighing performance gains against compute, infrastructure, and development cost --- relevant to deployment decisions, but not what 'evaluating model performance' itself measures.

Rigorous evaluation in this context requires a held-out test set the models were not trained or tuned on, appropriate metric selection for the task, and often statistical significance testing when comparing close results.

Answer : D

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.