AT-420
Free AI+ Robotics Practitioner Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$2499 exam domainsLevel Intermediate2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A warehouse robot uses a camera mounted above its gripper to identify box dimensions before adjusting its grip strength. Which stage of the perception-plan-act loop is responsible for interpreting the raw camera data into usable information such as box edges and size?
Perception is the stage where raw sensor data (camera images) is processed and interpreted into structured information the robot can use, such as object dimensions and position.
A warehouse mobile robot autonomously navigates predefined aisles, avoids dynamic obstacles, and re-routes around blocked paths on its own. However, when it enters an unmapped area outside its operational design domain, it stops and requests a human operator to take control. Which autonomy level BEST describes this robot's behavior?
Level 3 (conditional automation) means the system independently handles most driving/operational tasks within its design domain but explicitly hands control back to a human when it encounters situations outside that domain - exactly matching the scenario's unmapped-area handoff.
A robotics engineer is implementing a pick-and-place routine on a robot arm using ROS 2. The task takes a variable amount of time to complete, the client needs periodic progress feedback while it runs, and the client must be able to cancel the task mid-execution. Which ROS 2 communication mechanism BEST fits this requirement?
ROS 2 actions are purpose-built for long-running, preemptible tasks. They provide a goal request, continuous feedback messages during execution, a final result, and a built-in cancel mechanism — matching all three requirements.
A mobile robot operating in a warehouse must identify and localize multiple obstacle types (pallets, forklifts, people) in real time from a single RGB camera feed, under a strict 30ms inference budget on an embedded GPU. Which approach BEST meets these requirements?
Single-stage CNN detectors are specifically designed for real-time multi-class object detection and localization, balancing accuracy and low-latency inference suitable for embedded GPUs.
A team is training a reinforcement learning policy for a robotic arm to perform a multi-step assembly task. The only reward signal is +1 when the assembly is fully completed and 0 otherwise. After 500,000 training steps, the agent has not learned any useful behavior. Which approach BEST addresses this problem?
Sparse terminal rewards make credit assignment nearly impossible over long horizons. Reward shaping introduces dense intermediate signals (e.g., progress toward grasp, correct alignment) so the agent gets feedback throughout the episode, which is the standard fix for sparse-reward robotic manipulation tasks.
A home-assistant robot uses an LLM to interpret the spoken command "bring me the thing I was drinking from earlier." The LLM generates a plausible-sounding but factually incorrect reference to a mug that was never used that day. Which mitigation BEST addresses this failure mode in LLM-driven robot instruction?
Grounding LLM outputs in sensor data, scene memory, and world state is the standard technique to reduce hallucination and ensure the robot's generated plan maps to verifiable reality before action execution.
A fulfillment center operates with human pickers also present on the warehouse floor, aisles reconfigured weekly, and inventory locations that shift with demand. Robots must navigate dynamically, re-route around obstacles and people, and travel to changing destinations to ferry totes to packing stations. Which robot category BEST fits this requirement?
AMRs use onboard sensors (LiDAR/cameras) and SLAM-based mapping to build and update an understanding of their environment in real time, allowing them to re-route around people and obstacles and reach changing destinations without reprogrammed tracks — exactly what a dynamic, human-populated, reconfigurable warehouse requires.
A robotics startup wants a single model that can take a natural-language instruction like "pick up the red cup and place it on the shelf" along with a live camera feed, and directly output robot joint/gripper commands. Which class of model BEST fits this requirement?
VLA models are trained end-to-end on vision, language, and action data so they can map natural-language instructions and visual context directly to low-level robot actions, which is the emerging approach for generalist robot control.
A warehouse robotics company deploys autonomous forklifts that operate alongside human workers. During testing, a forklift fails to stop in time when a worker unexpectedly enters its path, causing a near-miss. Which design change BEST addresses the underlying safety gap according to responsible robotics deployment principles?
Safety-rated redundant perception, guaranteed stopping distance, and defined human oversight/exception handling are core requirements of physical safety design for autonomous systems operating near humans, directly addressing the failure mode observed.
An outdoor mobile robot must measure precise distances to obstacles across a 50-meter range while operating in direct sunlight. Which sensor type BEST meets this requirement?
LiDAR provides precise, long-range distance measurements and is widely used outdoors because it is far less affected by ambient light interference than infrared sensors and provides better accuracy than ultrasonic at longer ranges.
In intelligent agent architecture design, what is the primary distinguishing characteristic of a deliberative (planning-based) agent compared to a purely reactive agent?
Deliberative agents reason over an internal representation of the world state and search/plan a sequence of actions to achieve a goal before executing, in contrast to agents that react immediately to stimuli.
A team needs to generate large volumes of synthetic training data for robot perception, including photorealistic camera imagery and physically accurate LiDAR simulation, scaled across GPU clusters. Which simulation environment covered in the AT-420 curriculum is conceptually best suited for this?
Isaac Sim, built on NVIDIA Omniverse, is designed for photorealistic rendering, physically-based sensor simulation (cameras, LiDAR), and GPU-accelerated synthetic data generation at scale for training perception models.
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How much does the AT-420 exam cost?
The exam fee is approximately $249 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 9 domains: Robotics and AI Foundations (~11%), Autonomous Systems and Intelligent Agents (~6%), AI and Robotics Development Frameworks (~9%), Deep Learning for Robotics (~9%), Reinforcement Learning for Robotics (~9%), Generative AI and NLP for Human-Robot Interaction (~18%), Applied Robotics: Use-Cases and RPA (~17%), Emerging Technologies and Future Trends (~15%), AI Ethics, Safety, and Policy in Robotics (~6%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.