Common Traps on Show IP Route Output Practice Questions
- ·Longest-prefix match is checked before administrative distance.
- ·Connected and local routes can appear alongside dynamic or static routes.
- ·The selected route may not be the one with the lowest metric if the prefix length differs.
Sample Questions
Practice all 15 →A data scientist has trained a binary classification model to predict whether an email is spam (positive) or not spam (negative). On a test set, the model correctly identifies 90 out of 100 actual spam emails and 80 out of 100 actual non-spam emails. Which metric shows the proportion of actual spam emails that the model correctly predicted?
Explanation: Recall (also called sensitivity) measures the fraction of positive instances correctly identified. For this model, recall = 90 / (90 + 10) = 0.9. Accuracy is overall correctness, precision measures correctness of positive predictions, and F1 combines precision and recall.
A data science team trains a regression model to predict house prices. They evaluate the model using Mean Absolute Error (MAE). After deployment, they notice that the model occasionally produces large errors (e.g., underpredicting a luxury home by $500,000) while most predictions are within $20,000. The business is more concerned about the impact of these large errors than the average small error. Which additional metric should the team use to better capture the penalty for large errors?
Explanation: Root Mean Squared Error (RMSE) squares the errors before averaging, which gives disproportionately higher weight to large errors. This makes RMSE a better metric when large errors are especially undesirable. In contrast, MAE treats all errors equally, so the large errors are buried in the average. Mean Absolute Percentage Error (MAPE) and R-squared do not specifically penalize large errors more than small ones. R-squared measures the proportion of variance explained, not the magnitude of errors.
A bike-sharing company wants to predict the number of rentals per hour. Their model's predictions are usually close but occasionally have large errors due to unexpected events like sudden rain. They want a metric that heavily penalizes these large errors to ensure the model is not overly confident. Which evaluation metric should they primarily use?
Explanation: Mean Squared Error (MSE) squares the differences between predicted and actual values, so large errors are penalized much more heavily than small ones. This makes MSE suitable when large errors are particularly undesirable. Mean Absolute Error (MAE) penalizes all errors linearly, so it does not amplify the impact of outliers. Classification Accuracy is used for classification problems, not regression. R-squared measures the proportion of variance explained by the model but does not specifically penalize large errors. Therefore, MSE is the best metric for this requirement.
A bank uses a machine learning model to predict credit card fraud. The model's output is a probability score. The business wants to minimize the number of false positives (legitimate transactions incorrectly flagged as fraud) because these cause customer dissatisfaction. At the same time, they must also catch most fraudulent transactions. Which metric should the bank optimize to balance these two goals?
Explanation: F1 score is the harmonic mean of precision and recall, providing a single metric that balances both. Precision focuses on reducing false positives, and recall focuses on catching fraud. Accuracy is misleading when fraud is rare. Optimizing just precision would miss fraud; optimizing just recall would increase false positives. Therefore, F1 is the best choice for this trade-off.
A botanist uses Azure Automated Machine Learning to train a model that classifies iris flowers into three species: setosa, versicolor, and virginica. The dataset contains exactly 50 examples of each species, making it perfectly balanced. The botanist wants the primary metric to give equal importance to the classification performance of each species, regardless of their frequency. Which primary metric should the botanist select in Azure AutoML?
Explanation: Macro F1 calculates the F1 score for each class independently and then averages them, giving equal weight to each class's performance. In a balanced dataset, other metrics may yield similar numbers, but the specification of 'equal importance to each species' explicitly points to macro F1 as the correct metric. Accuracy treats each prediction equally, not each class, and weighted/micro F1 are influenced by class frequencies.
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Frequently asked questions
How do "Show IP Route Output Practice Questions" appear on the real AI-900?
Practise interpreting routing-table output, route selection, administrative distance, metrics, next hops and longest-prefix match. These appear throughout the AI-900 and require you to apply your knowledge, not just recall facts.
How many scenario questions are on the AI-900 exam?
Cisco doesn't publish an exact breakdown, but scenario-based questions (especially exhibit and command-output formats) make up a significant portion of the AI-900. Practicing each scenario type ensures you're ready for any format.
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