AIP-210
Free Certified Artificial Intelligence 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 ~$3684 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 financial services company has developed a new fraud-detection model and wants to evaluate its real-world performance before it starts making decisions that affect customers. The team wants to send live production traffic to the new model and compare its predictions against the current production model, but only the current model's outputs should be used to make actual decisions. Which deployment strategy BEST meets this requirement?
Shadow deployment routes live traffic to the new model in parallel with the production model so its predictions can be logged and compared, without those predictions ever being used to make customer-facing decisions.
A retail company wants to predict which customers are likely to stop purchasing (churn) in the next 90 days using historical transaction and engagement data. Which type of AI/ML problem framing BEST fits this business need?
Churn prediction has a clear labeled outcome (churned vs. not churned) derived from historical data, making it a textbook supervised binary classification problem.
A data scientist is training a fraud detection model where only 0.5% of transactions in the dataset are fraudulent. The model achieves 99.4% accuracy on the test set but fails to catch most real fraud cases in production. Which evaluation approach BEST addresses this problem?
With severe class imbalance, accuracy is dominated by the majority class and is misleading. Precision, recall, F1, and the precision-recall curve directly measure how well the model identifies the minority (fraud) class, which is the actual business concern.
A retail company's customer dataset has 'Income' missing for 18% of records. Investigation shows these values are missing specifically because customers who earn high incomes tend to decline reporting it — the missingness pattern is Missing Not At Random (MNAR). Which approach is MOST appropriate for handling this missing data?
Because the missingness itself is informative (MNAR), an indicator flag captures that signal, and model-based multiple imputation estimates plausible values from other variables rather than assuming randomness, reducing bias.
A team maintains a deployed model that predicts customer churn. Over several months, model performance metrics (accuracy and F1 score) steadily decline, even though the statistical distribution of the input features has remained stable and the feature computation pipeline is unchanged. Which phenomenon is MOST likely occurring?
Concept drift occurs when the underlying relationship between input features and the target variable changes over time, causing performance to degrade even when the input feature distribution itself is stable.
A hospital wants to build a system that transcribes doctor-patient conversations into text notes in real time. Which AI capability category does this use case primarily require?
Converting spoken audio into text in real time is the core function of automatic speech recognition (ASR), a well-defined AI capability category tested on the exam.
An ML engineer needs to tune six hyperparameters for a deep neural network. Each training run takes approximately 8 hours, and the hyperparameter search space is large and continuous. Which hyperparameter optimization strategy is MOST appropriate given these constraints?
Bayesian optimization builds a probabilistic model of the objective function and uses past results to choose promising hyperparameter combinations, making it far more sample-efficient than grid or random search when each trial is expensive (8 hours) and the space is large and continuous.
A data scientist applies SMOTE to the entire imbalanced fraud detection dataset to generate synthetic minority-class samples, and only afterward splits the resulting data into training and test sets. What is the primary problem with this workflow?
SMOTE generates synthetic points by interpolating between minority-class neighbors; applying it before the split means synthetic training samples can be derived from records that end up in the test set, leaking test information into training and producing overly optimistic performance metrics. SMOTE should be applied only to the training fold, after splitting or within cross-validation.
A team is configuring a canary deployment for a new recommendation model on a Kubernetes-based serving platform. They want to minimize risk while still collecting statistically meaningful data on the new model's performance. Which configuration approach is the BEST starting practice?
Starting with a small, defined percentage of traffic and progressively increasing it based on monitored health metrics is the core principle of a canary rollout, limiting blast radius while still validating real-world behavior.
A team is scoping an AI project to detect defective parts on a manufacturing line using camera feeds. During the problem-framing phase, which factor is MOST critical to evaluate before committing to an image recognition approach?
Image recognition models require substantial labeled training data covering the classes to be detected; without labeled defective and non-defective images, an accurate model cannot be trained, making data availability the critical framing consideration.
During training of a deep learning model, the loss value oscillates wildly and occasionally becomes NaN after only a few epochs. Which configuration change is the MOST likely fix?
A learning rate that is too high causes the optimizer to take excessively large steps, leading to oscillating or diverging (NaN) loss. Lowering the learning rate and/or clipping gradients constrains the update size and stabilizes training.
A dataset includes a 'Zip_Code' feature with roughly 40,000 unique values. Which encoding technique is MOST appropriate to control dimensionality while retaining predictive information?
Target encoding replaces each high-cardinality category with a target-derived statistic, using smoothing/regularization to avoid overfitting on rare categories, controlling dimensionality while preserving predictive signal — the standard technique for very high-cardinality categorical features.
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How much does the AIP-210 exam cost?
The exam fee is approximately $368 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 4 domains: Operationalizing ML Models (30%), Understanding the AI Problem (26%), Training and Tuning ML Systems and Models (24%), Engineering Features for Machine Learning (20%). 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.