NCA-GENM Exam Questions Get Updated [2026] with Correct Answers [Q22-Q36]

Published date August 9, 2026

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NCA-GENM Exam Questions Get Updated [2026] with Correct Answers

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
Topic 1: Multimodal AI Systems	– Multimodal model design – Cross-modal learning 1. Text-image integration 2. Audio-visual understanding
Topic 2: Core AI and Machine Learning Fundamentals	– Machine learning basics 1. Supervised and unsupervised learning 2. Neural networks fundamentals
Topic 3: Responsible and Trustworthy AI	– Ethical AI principles – Bias and safety considerations
Topic 4: NVIDIA AI Ecosystem	– NVIDIA tools and frameworks 1. NeMo framework usage 2. GPU-accelerated AI workflows
Topic 5: Generative AI Concepts	– Generative models 1. Diffusion models 2. Transformers and LLM basics

QUESTION 22
Consider a system that generates captions for images, and a key metric is BLEU score. You observe that while the BLEU score is high, the generated captions often lack detailed descriptions of the objects and relationships within the image. Which of the following strategies would you employ to improve the descriptive richness of the generated captions?

Increase the beam size during decoding to explore a wider range of possible captions.

Train the model to minimize cross-entropy loss between predicted and ground truth captions.

Fine-tune the model using Reinforcement Learning with a reward function that encourages detailed descriptions, such as CIDEr or SPICE.

Reduce the size of the vocabulary to focus on the most common words.

Implement early stopping based solely on BLEU score during training.

Reinforcement Learning with reward functions like CIDEr or SPICE directly optimizes for metrics that correlate with human judgments of caption quality, including detail and descriptive richness. Increasing beam size (A) can improve fluency but doesn’t guarantee more detail. Minimizing cross-entropy (B) focuses on matching ground truth captions, which may not always be the most descriptive. Reducing vocabulary size (D) would limit the model’s ability to generate detailed descriptions. Early stopping based solely on BLEU (E) might lead to premature convergence on captions that score well on BLEU but lack detail.

QUESTION 23
Consider the following PyTorch code snippet for a GAN discriminator:

The code will raise a ‘ValueErroN’ because ‘torch.mean’ expects a ‘dim’ argument.

The code will train without errors, but the discriminator’s performance will be poor due to vanishing gradients.

The code implements a hinge loss, encouraging the discriminator to output values greater than 1 for real samples and less than -1 for fake samples.

The code implements a non-saturating loss, designed to alleviate vanishing gradients in the discriminator.

The code will train without errors, but there is no significant impact on the discriminator.

The code calculates the hinge loss. The loss for real samples is – , which penalizes the discriminator when the output for real samples is less than 1. The loss for fake samples is + fake_output))’ , which penalizes the discriminator when the output for fake samples is greater than -1. The ‘torch.mean’ function calculates the mean over all elements of the input tensor, so the ‘dim’ argument is not needed.

QUESTION 24
Given the following Python code snippet using Pandas, which is intended to filter rows where the ‘price’ column is greater than 100 and the ‘quantity’ column is less than 5, identify the correct approach to achieve this:

Option B uses the ‘query’ method for concise filtering. Option D correctly uses boolean indexing with the (and) operator within square brackets. Option E is syntactically incorrect because it uses the ‘and’ keyword, which is meant for single boolean values, not Pandas Series. Option A does logical OR which is not what we intend to filter. Option C is incorrecrt since filter method expects list of column names.

QUESTION 25
Consider a multimodal emotion recognition system that uses both facial expressions and speech audio as input. You want to fuse the information from these two modalities. Which of the following fusion techniques would be most suitable if the modalities have significantly different temporal resolutions (e.g., facial expressions change more rapidly than overall vocal tone)?

Early Fusion (concatenating raw features)

Late Fusion (averaging probabilities from individual classifiers)

Intermediate Fusion (using attention mechanisms to align features)

Decision Fusion (majority voting based on modality predictions)

Feature Extraction (extracting features)

Intermediate fusion, particularly with attention mechanisms, is well-suited for modalities with different temporal resolutions. Attention allows the model to dynamically align and weight the features from each modality based on their relevance at different time steps, addressing the temporal misalignment issue. Early fusion would be problematic as the temporal differences are not handled. Late fusion ignores the potential interactions between the modalities. Decision fusion suffers from the same issues as late fusion. Feature extraction is not fusion technique.

QUESTION 26
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

Fine-tune only the text encoder layers, keeping the image encoder layers frozen.

Fine-tune only the image encoder layers, keeping the text encoder layers frozen.

Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.

Train a new classification head from scratch on top of the frozen pre-trained model.

Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.

Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.

QUESTION 27
What is the purpose of a kernel in a Convolutional Neural Network (CNN)?

To perform convolution operations on input data.

To calculate the loss function.

To classify the data into different categories.

To normalize the input data.

A kernel (or filter) in a CNN is a small matrix of learnable weights that slides across the input (an image, feature map, or intermediate activation) computing a dot product at each spatial position – the convolution operation. Each kernel is trained to detect a specific local pattern: early-layer kernels typically learn to detect low-level features like edges and color gradients, while kernels in deeper layers combine these into detectors for more complex, higher-level patterns (textures, object parts, and eventually whole-object representations as receptive fields grow with depth). A convolutional layer typically applies many kernels in parallel, each producing its own output channel, collectively forming the layer’s feature map.
The other options describe separate CNN components with distinct responsibilities: the loss function (B) is computed at the network’s output based on the difference between predictions and ground truth, entirely separate from the kernel’s role in feature extraction. Classification (C) is typically performed by fully connected (dense) layers – often with a softmax activation – placed after the convolutional feature- extraction stack, not by the kernels themselves. Normalization (D) is handled by dedicated layers such as batch normalization or layer normalization, inserted between convolutional layers to stabilize activations, again a separate mechanism from the convolution operation itself.
Reference: Core Machine Learning and AI Knowledge domain – CNN architecture, kernels/filters, feature extraction.

QUESTION 28
You are working with a large multimodal dataset that contains images and corresponding text descriptions. The text descriptions are highly variable in length and content. Which of the following techniques is MOST effective for handling this variability when training a multimodal model?

Pad all text descriptions to the same maximum length using a special padding token.

Truncate all text descriptions to a fixed length.

Use dynamic padding and masking to handle variable-length sequences efficiently during batch processing.

Ignore text descriptions that are longer than a certain threshold.

Create a fixed-size vocabulary and discard any words not in the vocabulary.

Dynamic padding and masking allow the model to efficiently process variable-length sequences without losing information or introducing bias. Padding to a fixed length can waste computation, truncating loses data and ignoring descriptions also loses information. Discarding words could harm the model’s learning abilities.

QUESTION 29
You are deploying a multimodal generative A1 model using Triton Inference Server. The model takes both image and text inputs. Which of the following approaches is most suitable for handling the preprocessing and postprocessing steps within Triton?

Performing all preprocessing and postprocessing on the client-side before sending the data to Triton and after receiving the results.

Implementing the preprocessing and postprocessing logic within the model itself as part of the neural network architecture.

Using Triton’s ensemble models to chain preprocessing, the core generative model, and postprocessing models together.

Writing custom C++ code to handle preprocessing and postprocessing within Triton’s backend.

Relying solely on Triton’s automatic data type conversion capabilities without implementing any explicit preprocessing or postprocessing.

Triton’s ensemble models provide the most flexible and scalable way to handle preprocessing and postprocessing. By creating separate models for these steps and chaining them together with the core generative model, you can easily manage complex pipelines and optimize each stage independently. Client-side processing (A) increases client burden. Embedding logic in the model (B) limits flexibility. Custom C++ code (D) is complex. Relying solely on automatic conversion (E) is often insufficient.

QUESTION 30
You are building a multimodal application that takes an image and a short text description as input and generates a more detailed text description of the image. Which of the following model architectures is BEST suited for this task?

A simple CNN followed by an LSTM.

A Vision Transformer (ViT) for image encoding and a Transformer for text decoding.

A Recurrent Neural Network (RNN) with attention mechanisms.

A Generative Adversarial Network (GAN) with separate image and text encoders.

A Multilayer Perceptron (MLP) for both image and text.

A Vision Transformer (ViT) excels at encoding image information, and a Transformer architecture is highly effective for text generation. The combination allows for effective processing of both modalities and generation of coherent, detailed text descriptions based on the image content and initial text prompt. CNN+LSTM could work, but is generally less performant. RNNs struggle with long-range dependencies. GANs are not ideal for this specific text generation task. MLPs don’t capture the sequential dependencies well.

QUESTION 31
You have been given a dataset with missing values. What is the first step you should take with the data?

Analyze the patterns and distribution of missing values.

Remove the rows with missing values.

Fill in the missing values with a default value.

Remove the columns with missing values.

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.
Reference: Data Analysis and Visualization domain – missing data diagnosis (MCAR/MAR/MNAR) prior to imputation strategy selection.

QUESTION 32
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

Increasing the complexity of the model.

Adding Ll or L2 regularization.

Using data augmentation techniques.

Reducing the amount of training data

Implementing dropout layers.

Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.

QUESTION 33
You’re developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?

Increase the learning rate for the audio modality pathway during training.

Apply modality-specific dropout to the image pathway.

Oversample the audio data during training.

Reduce the dimensionality of the image features before fusion.

Increase the batch size for each epoch.

Increasing the learning rate for the audio pathway allows it to update its weights more aggressively, potentially counteracting the image bias. Applying modality-specific dropout to the image pathway forces the model to rely less on image features and more on audio. Oversampling the audio data ensures that the model sees more examples from the audio modality during training. Reducing the dimensionality of the image features can prevent them from dominating the fusion process. Increasing batch size is not specific to each modality and does not directly deal with modality imbalance, but can influence training dynamics.

QUESTION 34
You have a multimodal model that takes video and audio as input for activity recognition. You want to evaluate the impact of different fusion strategies (early fusion, late fusion, intermediate fusion) on the model’s accuracy and computational cost. Which of the following statements is generally TRUE regarding these fusion strategies?

Early fusion typically has the lowest computational cost but may limit the model’s ability to capture modality-specific features.

Late fusion typically has the highest computational cost but allows for the most effective interaction between modalities.

Intermediate fusion is always superior to both early and late fusion in terms of accuracy.

Early fusion is always the best choice for real-time applications due to its low latency.

Late fusion generally easier to implement than early fusion as it doesn’t require modification to the individual modality encoders.

Early fusion concatenates the input features early in the network, reducing computational complexity. However, it may not effectively capture modality-specific nuances. Late fusion combines modality-specific predictions, allowing for independent processing but potentially missing early interactions. Intermediate fusion offers a balance, but the optimal strategy depends on the specific task and data characteristics.

QUESTION 35
What is the purpose of the cuDNN library?

To generate images from English text-prompts using CLIP.

To measure GPU usage and other metrics with Prometheus.

To optimize deep neural network computations on NVIDIA GPUs.

To implement GPU-accelerated data preparation and feature extraction.

cuDNN (CUDA Deep Neural Network library) is NVIDIA’s GPU-accelerated library providing highly optimized, low-level implementations of the primitive operations that underpin deep learning – convolutions, pooling, normalization, activation functions, and recurrent operations – tuned specifically for NVIDIA GPU architectures. Deep learning frameworks including PyTorch, TensorFlow, and JAX call into cuDNN under the hood rather than implementing these operations themselves, which is why upgrading a GPU driver/cuDNN version can materially change training and inference performance without any change to model code.
cuDNN’s optimizations include algorithm auto-tuning (selecting the fastest available convolution algorithm for a given tensor shape and hardware), Tensor Core utilization for mixed-precision workloads, and kernel- level performance engineering that individual framework developers would find impractical to reimplement and maintain for every GPU generation.
The distractors point to different, specific NVIDIA-ecosystem or third-party tools: text-to-image generation via CLIP (A) is an application-level generative task, not a low-level compute library’s function. GPU metrics monitoring via Prometheus (B) describes observability tooling (commonly paired with NVIDIA’s DCGM exporter), a separate concern from computational optimization. GPU-accelerated data preparation (D) more closely describes RAPIDS libraries like cuDF, not cuDNN, which is specifically scoped to neural network primitive operations rather than general data preprocessing.
Reference: Performance Optimization domain – cuDNN, GPU-accelerated deep learning primitives.

QUESTION 36
You’re working with a multimodal model that fuses text and image features. You’ve noticed that the model performs poorly when the text and image are semantically misaligned (e.g., an image of a dog and the caption ‘a cat on a mat’). Which of the following techniques can help improve the model’s robustness to such misalignment?

Increasing the learning rate during training.

Adding a contrastive loss that penalizes embeddings of misaligned text-image pairs.

Decreasing the batch size.

Using only positive text-image pairs for training.

Removing dropout layers from the model architecture.

A contrastive loss function directly addresses the issue of semantic misalignment by penalizing the model when it produces similar embeddings for text and images that don’t correspond semantically. This encourages the model to learn more robust and meaningful feature representations.

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