Cisco AI Technical Practitioner Exam Practice Test
The Cisco AI Technical Practitioner Exam practice page covers foundational AI concepts used to build, evaluate, and operate AI applications. Its question bank includes model training and evaluation, classification metrics, inference patterns, generative AI reliability, privacy, interpretability, and response-time measurement. These concepts support practical decisions about selecting models, interpreting results, and managing AI features in software systems.
Exam overview
About the Cisco AI Technical Practitioner Exam
The practice topics connect machine-learning methods to application-level decisions. They include distinguishing regression from classification, using precision and recall to assess classification outcomes, and separating training data from data reserved for final evaluation. Neural-network items address how backpropagation calculates gradients and how optimizers use them to reduce loss. Other scenarios focus on choosing online inference when requests must be handled as they arrive, limiting personal information sent to an external model, and recognizing unsupported generated claims as hallucinations. Interpretability questions concern making recommendations understandable to reviewers, while latency questions focus on the time a user waits for a response. Together, these subjects call for reading a technical scenario, identifying the relevant AI concept or measurement, and relating it to model quality, privacy, or application behavior.
Sample Practice Questions
Review a selection from this exam's published US Practice Tests practice bank.
An AI application sends user prompts to an external model provider. Which measure most directly helps limit exposure of personal information in those prompts?
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Correct answer: BRemoving or masking unnecessary personal information reduces the amount of sensitive data transmitted to the external service. Data handling should also follow applicable organizational policies and legal requirements.
A generative AI assistant sometimes presents plausible statements that are not supported by its reference documents. What is this behavior commonly called?
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Correct answer: BA hallucination is generated content that is inaccurate or unsupported, even when it sounds convincing. Grounding and verification can help reduce this risk.
A classifier correctly identifies most legitimate transactions but misses many fraudulent ones. Which metric should the team examine to understand the proportion of actual fraud cases it detects?
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Correct answer: ARecall is the fraction of actual positive cases that the classifier identifies. For fraud detection, it measures how many fraudulent transactions are caught.
An application should classify an incoming support request as it arrives, without waiting to collect a batch of requests. Which inference pattern fits this need?
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Correct answer: AOnline inference returns predictions for individual requests as they arrive. Batch inference processes groups of inputs, often on a schedule or asynchronously.
A neural network produces a prediction and a loss value during training. What does a gradient-based optimizer use the loss gradients for?
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Correct answer: AThe optimizer uses gradients, which indicate how parameter changes affect the loss, to update model parameters. This iterative process aims to reduce the chosen training objective.
A team wants a model to estimate the numeric selling price of a home from its features. Which type of task is this?
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Correct answer: BRegression predicts a continuous numeric value, such as a home's selling price. Classification instead predicts a discrete category.
When training a neural network, what is the main role of backpropagation?
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Correct answer: ABackpropagation computes gradients of the loss with respect to network parameters. An optimizer uses those gradients to update the parameters during training.
How to use this practice test
Access the free practice questions and complete a session of up to 20 randomly selected items from the 50-question bank. After the session, use the results to identify concepts that need more attention, such as evaluation metrics, training mechanics, inference, privacy, or generative AI reliability. Revisit missed items by explaining the underlying decision in your own words.
Preparation tips
- For classification scenarios, distinguish precision from recall and connect recall to the share of actual positive cases detected.
- Trace a neural-network training step: relate loss gradients to backpropagation and explain how an optimizer uses them to update parameters.
- Practice identifying data leakage risks by separating training data from the holdout set used for final model evaluation.
- Compare batch and online inference using request timing and workload needs; connect response latency to the time an individual user waits.
- For generative AI scenarios, distinguish unsupported output from grounded responses and consider privacy controls before sending prompts to an external provider.