AI0-001 Practice Questions 1 — 50 Questions

Question 1

A machine learning engineer is building a spam filter. The dataset contains 10,000 emails, of which 1,000 are spam. The engineer decides to use a Random Forest classifier. Which preprocessing step is most critical to ensure the model generalizes well to new, unseen emails?

Accepted Answer

Split the data into training and testing sets before any other preprocessing. Option C is correct because splitting the data into training and testing sets before any other preprocessing prevents data leakage. If preprocessing like normalization or PCA is applied to the entire dataset first, the test set information influences the training process, leading to overly optimistic performance estimates and poor generalization to new, unseen emails.

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Apply Principal Component Analysis (PCA) to reduce dimensionality

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Normalize the numerical features to have zero mean and unit variance

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Encode all features using one-hot encoding

Question 2

Which THREE are common data preprocessing steps in a machine learning pipeline? (Choose 3)

Accepted Answer

Encoding categorical variables. Encoding categorical variables is a common data preprocessing step because machine learning algorithms require numerical input. Techniques like one-hot encoding or label encoding convert categorical data (e.g., colors, countries) into numeric format, enabling the model to process them correctly. Without this step, the model would misinterpret categorical labels as ordinal or meaningless numeric values.

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Hyperparameter tuning

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Model evaluation

Question 3

An e-commerce company uses an AI system to set dynamic prices for products. A customer complains that the price they see is higher than the price shown to a friend for the same product at the same time. The company wants to ensure pricing fairness. Which ethical principle should guide the redesign of the pricing algorithm?

Accepted Answer

Transparency and explainability. Transparency and explainability is the correct principle because the core issue is that the customer cannot understand why the AI system set a different price for them compared to their friend. Redesigning the algorithm to provide clear, understandable reasons for price variations—such as demand, purchase history, or time of day—directly addresses this lack of visibility. This principle ensures that the system's decision-making process is open to scrutiny, which is essential for building trust and resolving fairness complaints in dynamic pricing models.

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Privacy by design

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Accountability

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Beneficence

Question 4

An AI system used for autonomous driving is found to have a lower accuracy in detecting pedestrians with darker skin tones. The development team wants to address this ethical issue. Which action is most effective?

Accepted Answer

Augment the training dataset with more images of pedestrians with darker skin. Option B is correct because augmenting the training dataset with more images of pedestrians with darker skin directly addresses the root cause of the bias: underrepresentation in the training data. By providing a more balanced and diverse dataset, the model can learn more robust features for all skin tones, reducing accuracy disparity without altering the algorithm's core logic or introducing arbitrary thresholds.

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Conduct additional testing to measure the disparity

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Replace the object detection algorithm with a different one

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Adjust the model's decision threshold for pedestrian detection

Question 5

In the AI lifecycle, which phase involves splitting data into training, validation, and test sets?

Accepted Answer

Data preprocessing. Data preprocessing is the phase where raw data is cleaned, transformed, and prepared for modeling. Splitting the dataset into training, validation, and test sets is a critical step during this phase to ensure unbiased evaluation and prevent data leakage. This split occurs before any model training begins, making it part of preprocessing rather than training or evaluation.

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Model training

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Data collection

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Model evaluation

Question 6

A startup is building a chatbot for customer service. They have 500 recorded conversations and want to use a pre-trained language model to generate responses. However, they have limited computational resources and need the chatbot to respond in real-time. They are considering fine-tuning a large model like GPT-3 or using a smaller model like DistilBERT. The conversation data contains industry-specific jargon. Which approach should they take?

Accepted Answer

Fine-tune DistilBERT on the conversation data. Option B is correct because fine-tuning DistilBERT on the 500 recorded conversations allows the model to adapt to industry-specific jargon while maintaining real-time responsiveness due to its smaller size. DistilBERT is a distilled version of BERT that retains 97% of BERT’s language understanding with 40% fewer parameters, making it suitable for limited computational resources. Fine-tuning on domain-specific data is essential here, as pre-trained models like GPT-3 lack exposure to the startup’s specialized terminology, and using a smaller model ensures low-latency inference for real-time chatbot responses.

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Use GPT-3 via API without fine-tuning

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Train a custom RNN from scratch on the conversations

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Implement a rule-based system with keywords

Question 7

A data scientist is preparing a dataset for supervised learning. Which TWO steps are essential?

Accepted Answer

Labeling the data. Labeling the data is essential for supervised learning because the algorithm requires input-output pairs to learn a mapping function. Without labeled data, the model cannot be trained to predict outcomes, as supervised learning relies on ground-truth targets for error correction during training.

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One-hot encoding all features

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Normalizing features

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Removing outliers

Question 8

A company wants to create an AI system that can identify objects in images. They have a large dataset of labeled images. Which type of neural network architecture is most suitable?

Accepted Answer

Convolutional neural network (CNN). Convolutional neural networks (CNNs) are specifically designed to process grid-like data such as images. They use convolutional layers to automatically learn spatial hierarchies of features (edges, textures, objects) from pixel data, making them the most suitable architecture for image classification tasks with labeled datasets.

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Transformer

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Generative adversarial network (GAN)

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Recurrent neural network (RNN)

Question 9

A financial services company is developing an AI model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent ones. Which technique should the data scientist use to address the class imbalance problem?

Accepted Answer

Apply Synthetic Minority Oversampling Technique (SMOTE). SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic examples of the minority class (fraudulent transactions) by interpolating between existing minority instances, rather than duplicating them. This addresses the extreme 0.1% fraud rate without introducing overfitting or losing data, making it a standard technique for imbalanced classification problems in financial fraud detection.

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Use a bagging ensemble method

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Undersample the legitimate transactions

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Use cost-sensitive learning with higher weight on fraudulent class

Question 10

Based on the exhibit, which action is most likely to resolve the memory issue?

Accepted Answer

Reduce the batch size.. The exhibit shows an out-of-memory (OOM) error during training. Reducing the batch size decreases the memory footprint per iteration, allowing the model to fit within available GPU memory. This directly resolves the memory issue without altering the model architecture or data.

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Add more training data.

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Increase the learning rate.

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Switch to a CPU.

Question 11

A company deploys an AI model via a REST API that handles sensitive customer data. To secure the endpoint, the security team requires that only authenticated and authorized applications can invoke the API. Which mechanism should be implemented?

Accepted Answer

API key or bearer token in the HTTP header. Option A is correct because API keys or bearer tokens (e.g., OAuth 2.0 access tokens) are the standard mechanism for authenticating and authorizing client applications when invoking a REST API. These tokens are passed in the HTTP Authorization header, allowing the server to verify the client's identity and permissions before processing requests containing sensitive customer data.

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TLS encryption for the connection

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Input sanitization to prevent injection

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IP whitelisting

Question 12

During an AI model deployment, the operations team notices that inference requests are taking longer than expected. Which component is most likely causing the bottleneck?

Accepted Answer

The machine learning model's size and architecture. The machine learning model's size and architecture directly determine the computational complexity of inference. Larger models with more parameters or deeper architectures require more matrix multiplications and memory bandwidth, which increases latency per request. This is the most common bottleneck in AI deployment because the model itself is the core computation unit, and its inference time scales with its complexity.

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Input data preprocessing pipeline

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API gateway rate limiting

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Database connection pool size

Question 13

During model monitoring, a loan approval model shows disparate impact against a protected group. The model's overall accuracy is high, but the false positive rate for the protected group is 0.12 compared to 0.02 for other groups. Which action should the operations team take first?

Accepted Answer

Retrain the model with reweighted training data to minimize disparity. Option C is correct because retraining the model with reweighted training data directly addresses the root cause of disparate impact—biased historical data—by assigning higher weights to underrepresented groups during training. This technique, often implemented via cost-sensitive learning or sample reweighting, adjusts the model's internal decision boundaries to reduce false positive rate disparities without sacrificing overall accuracy. The operations team should first attempt to mitigate bias at the data level before considering threshold adjustments or model replacement, as reweighting preserves the model's learned patterns while promoting fairness.

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Document the disparity and proceed with deployment because accuracy is high

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Replace the model with a simpler model that is less discriminatory

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Adjust the decision threshold for the protected group to equalize false positive rates

Question 14

A healthcare company must deploy a diagnostic AI model that uses protected health information (PHI). To comply with HIPAA, the operations team needs to ensure data privacy during model inference. Which practice should be implemented?

Accepted Answer

Encrypt all PHI at rest and in transit within the inference pipeline. Option B is correct because HIPAA mandates encryption of protected health information (PHI) both at rest and in transit to safeguard data confidentiality during model inference. Encrypting the entire inference pipeline ensures that even if data is intercepted or accessed without authorization, it remains unreadable. This practice directly addresses the compliance requirement for data privacy without relying on network location or partial obfuscation.

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Run the model on-premises to avoid cloud data transmission

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Mask sensitive fields in the input data before inference

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Apply differential privacy during model training only

Question 15

A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?

Accepted Answer

Apply cost-sensitive learning. Cost-sensitive learning directly modifies the model's loss function to penalize misclassifications of the minority class more heavily than those of the majority class. This approach addresses the root cause of the imbalance—the model's bias toward the majority class—without altering the dataset distribution, making it the most immediate and effective first step.

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Reduce the majority class size

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Use SMOTE to generate synthetic samples

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Collect more data for the minority class

Question 16

An MLOps team automates model deployment with a CI/CD pipeline. A performance regression is detected after deploying a new model version. The team needs to automatically roll back to the previous version. Which approach best enables safe automated rollback?

Accepted Answer

Use a blue/green deployment with automated health checks and traffic switching. Blue/green deployment with automated health checks and traffic switching is the best approach because it allows the team to instantly route all traffic back to the previous (green) environment if the new (blue) version fails health checks. This ensures zero-downtime rollback without manual intervention, directly addressing the need for safe automated rollback in a CI/CD pipeline.

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Maintain a manual rollback script that the operations team can run

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Deploy new models as canary releases and monitor for 24 hours

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Automatically keep the previous model version in storage for later use

Question 17

Refer to the exhibit. A team created an access policy for a fraud detection model endpoint. An intern reports being unable to access the model for testing. Reviewing the policy, what is the most likely cause?

Accepted Answer

The intern's role is explicitly denied in the policy. Option D is correct because the exhibit shows an explicit `Deny` effect for the intern's role in the policy. In AWS IAM (or similar cloud provider) access policies, an explicit deny overrides any allow, so even if the intern's role is listed in allowed roles, the explicit deny will block access. This is a fundamental principle of IAM policy evaluation logic.

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The intern's role is not included in the allowed roles

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The policy JSON has a syntax error

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The endpoint path is incorrect

Question 18

A dataset for a binary classification problem has 95% of samples in class "0" and 5% in class "1". The data scientist trains a logistic regression model and achieves 95% accuracy. Which metric should the scientist primarily use to evaluate model performance?

Accepted Answer

Precision, recall, and F1-score.. In a highly imbalanced dataset (95% class 0, 5% class 1), accuracy is misleading because a model can achieve 95% accuracy by simply predicting the majority class for all samples. Precision, recall, and F1-score provide a more nuanced view of performance on the minority class, which is typically the class of interest in binary classification problems. The F1-score, in particular, balances precision and recall, making it the primary metric for evaluating model effectiveness on imbalanced data.

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R-squared.

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Accuracy.

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Mean squared error.

Question 19

A data scientist is evaluating a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). Which TWO metrics are most appropriate for assessing model performance? (Choose two.)

Accepted Answer

Precision. Precision is appropriate because it measures the proportion of predicted fraud cases that are actually fraudulent, which is critical when false positives (flagging legitimate transactions as fraud) are costly. In a highly imbalanced dataset like this (99% non-fraud), precision directly evaluates the model's ability to avoid overwhelming fraud analysts with false alarms.

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F1 score

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Area under the ROC curve (AUC-ROC)

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Accuracy

Question 20

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

Accepted Answer

Time-series database. Time-series databases (TSDBs) are optimized for high-ingest rates of timestamped data and provide efficient downsampling, retention policies, and time-based aggregation functions. For IoT sensor streaming, a TSDB like InfluxDB or TimescaleDB delivers sub-second query performance on time-range scans, which is essential for real-time analytics.

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Data warehouse

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Relational database

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Data lake