Free 10-Question NCA-AMAD Quick-Start Practice Test

Correct answer: C. The agent is goal-directed and plans and executes multi-step tasks using tools

C is correct because traditional generative AI apps follow a request-and-respond pattern, while agents coordinate models with external tools to plan and carry out multi-step work toward a goal. A is incorrect because agents are defined by their use of tools, APIs and data sources, the opposite of answering from training data alone. B is incorrect because a fixed decision tree describes a rule-based system; agents reason and choose their own steps. D is incorrect because model size does not define an agent; agents can run on small open models or frontier models alike.

Correct answer: B. Replaces each category with a target statistic computed for that category

B is correct because target encoding is a groupby aggregation where the aggregated column is the target, such as the mean price for each brand, so a category becomes a number the model can use. A is incorrect because that is binning, which reduces the precision of a numeric column rather than encoding a category with the target. C is incorrect because that describes one-hot encoding, which creates one column per category and never looks at the target. D is incorrect because that is standardization, a scaling step for numeric features that uses neither the target nor the categories.

Correct answer: C. A distributed layer for disaggregated serving, KV-aware routing and autoscaling

C is correct because Dynamo leaves the single-GPU forward pass to the engine and adds the system layer around it: disaggregated prefill and decode, KV cache-aware routing, KV cache offloading across memory tiers and SLA-based autoscaling. A is incorrect because post-training quantization is done with TensorRT Model Optimizer; Dynamo serves models but does not quantize them. B is incorrect because Dynamo is an inference serving framework; distributed training is the job of tools such as Megatron-LM and the NeMo framework. D is incorrect because Dynamo is engine-agnostic and runs on top of vLLM, SGLang and TensorRT-LLM rather than replacing them.

Correct answer: B. It avoids the extra inference latency of managing a separate adapter

B is correct because merging adds the low-rank update into the pretrained weights, giving a model that is structurally the same as the base model, so no separate adapter has to be handled at inference time. A is incorrect because the merged weights have the same shape and size as the original weight matrices; merging adds no compression. C is incorrect because once the adapter is merged it is part of the weights; swapping adapters at runtime is what dynamic loading provides. D is incorrect because a merged model becomes bespoke and serves only the task it was tuned for; mixed-task batching is the benefit of loading adapters dynamically.

Correct answer: A. It learns features from raw data through many stacked layers

A is correct because deep learning stacks layers of nonlinear transformations that learn features such as edges and shapes directly from raw data, so the features do not have to be engineered by hand first. B is incorrect because classical models such as gradient-boosted trees often match or beat neural networks on small tabular datasets. C is incorrect because neural networks are commonly trained on labeled data; whether a task is supervised is a choice of problem, not what separates them from classical models. D is incorrect because a neural network's behavior comes from weights adjusted during training, not from fixed hand-written rules.

Correct answer: B. It downloads model files ahead of time and keeps them on a persistent volume

B is correct because a NIMCache pulls the model profiles from NGC or another source and persists them on a PVC, so NIM pods can start from the cached copy instead of downloading at startup. A is incorrect because scaling pods is the job of a horizontal pod autoscaler, which the NIMService resource can create. C is incorrect because the cache stores model files on a volume; it does not build container images. D is incorrect because request distribution is handled by the Kubernetes service or an ingress, not by the cache resource.

Correct answer: C. A Jetson AGX Thor onboard computer

C is correct because the third computer is the robot's own runtime computer, Jetson AGX Thor, which performs inference on the robot's sensor data. A is incorrect because a cloud instance is not mounted on the robot and cannot provide real-time onboard inference. B is incorrect because Omniverse and Cosmos on RTX PRO Servers form the second computer, used for simulation and synthetic data. D is incorrect because DGX is the first computer, used to train the robot's AI models.

Correct answer: D. It evaluates real usage data and triggers retraining when quality slips

D is correct because the flywheel captures real-world usage, evaluates model performance and triggers retraining or fine-tuning as needed, which keeps the model aligned with current users and context. A is incorrect because a calendar swap ignores whether quality actually dropped and skips evaluation on the team's own data. B is incorrect because frozen weights are exactly what lets a model drift out of date as data and user behavior change. C is incorrect because temperature only changes output randomness; it does not teach the model anything new.

Correct answer: D. An agent harness

D is correct because NVIDIA describes the harness as the scaffolding that gives the LLM the ability to act: it connects short- and long-term memory, knows which tools are available and can create new skills if allowed. A is incorrect because the secure runtime is the policy-enforced sandbox in which the agent executes, and the stem says the prototype already has one. B is incorrect because the LLM already reasons well; a bigger model still could not reach memory or tools without the scaffolding around it. C is incorrect because a planning module breaks complex tasks into steps; it does not connect the LLM to memory or tools.

Correct answer: A. Exploratory data analysis

A is correct because exploratory data analysis (EDA) examines the data's quality, distributions and relationships before modeling, and these checks are its basic steps. B is incorrect because ensembling combines the predictions of several trained models, such as by hill climbing, so it needs trained models first. C is incorrect because hyperparameter tuning searches model settings after a model and a validation scheme already exist. D is incorrect because inference optimization speeds up a deployed model and does not examine the training data.