AI0-001 Practice Test 48 — 15 Questions

Question 1

A team is deploying a sentiment analysis model that must achieve high precision and high recall. They have a labeled dataset of 10,000 samples. They want to minimize overfitting. Which THREE actions are most appropriate? (Select THREE.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Apply L2 regularization to the model weights. L2 regularization (option B) penalizes large weights by adding a squared magnitude term to the loss function, which discourages the model from fitting noise in the training data. This directly reduces overfitting while maintaining high precision and recall by keeping the decision boundary smooth and generalizable.

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Decrease the learning rate

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Increase the training batch size

Question 2

A data engineer is preparing a dataset for a binary classification model. The dataset has 10,000 samples with 100 features. To improve model performance and reduce training time, the engineer decides to perform feature selection. Which two techniques are appropriate for this task? (Select TWO).

Accepted Answer

Recursive Feature Elimination (RFE). Recursive Feature Elimination (RFE) is an appropriate feature selection technique because it iteratively removes the least important features based on a model's feature importance scores or coefficients, directly reducing the feature count from 100 to a smaller subset. This improves model performance by eliminating irrelevant or redundant features and reduces training time by decreasing dimensionality.

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Normalization

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One-Hot Encoding

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Principal Component Analysis (PCA)

Question 3

Which TWO of the following are common threats to AI model security?

Accepted Answer

Data poisoning. B is correct because data poisoning involves an attacker injecting malicious data into the training set to corrupt the model's learning process, causing it to make incorrect predictions or classifications. This directly undermines the integrity of the AI model by manipulating its foundational training data.

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SQL injection

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Distributed denial-of-service (DDoS)

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Phishing attacks

Question 4

A cybersecurity analyst monitors an AI chatbot that frequently produces offensive responses when given specific prompts. The development team suspects an adversarial attack. Which mitigation strategy is most effective against such prompt injection attacks?

Accepted Answer

Implement input validation and sanitization. Prompt injection attacks exploit the model's inability to distinguish between user input and system instructions. Input validation and sanitization (e.g., filtering special characters, enforcing strict schema checks, and using allowlists) directly neutralize malicious payloads before they reach the model's inference engine, preventing the model from executing unintended commands.

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Retrain the model on a larger, curated dataset

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Encrypt all communication between users and the chatbot

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Reduce the model's number of parameters

Question 5

An AI team notices that a model's F1 score on the validation set is 0.95, but on the test set it drops to 0.72. Which course of action is most appropriate?

Accepted Answer

Apply regularization.. The F1 score dropping from 0.95 on the validation set to 0.72 on the test set is a classic sign of overfitting, where the model has memorized the training/validation data but fails to generalize to unseen test data. Applying regularization (e.g., L1/L2 weight decay, dropout) is the most appropriate course of action because it penalizes overly complex models, reduces variance, and improves generalization without requiring more data or changing the model architecture.

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Reduce the training dataset size.

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Adjust the train/test split to be more representative.

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Increase model complexity.

Question 6

A team is developing an AI agent that can answer questions by querying a SQL database and a REST API. The agent should decide which tool to call, parse the response, and reason about the next step. Which THREE concepts should be implemented to build this agent?

Accepted Answer

ReAct pattern for iterative reasoning and tool use. The ReAct pattern (Reasoning + Acting) enables iterative tool selection. Function calling allows the LLM to output structured tool calls. Planning agents can decompose a question into subtasks. Chain-of-thought is a reasoning technique but not a full agent framework; content filtering is not needed.

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Content filtering to sanitize database results

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Chain-of-thought prompting without tool integration

Question 7

Which THREE are common activation functions used in neural networks? (Choose THREE.)

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a common activation function in neural networks because it introduces non-linearity while being computationally efficient. It outputs the input directly if positive, otherwise zero, which helps mitigate the vanishing gradient problem compared to sigmoid or tanh. This makes it a default choice for hidden layers in many deep learning architectures.

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Softmax

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Linear

Question 8

A data engineer is designing a pipeline for a streaming data application that uses a machine learning model to detect anomalies in real time. Which TWO practices should the engineer implement to ensure data quality and model reliability?

Accepted Answer

Use a sliding window for feature computation. Streaming anomaly detection requires real-time feature computation over recent data, and a sliding window ensures that only the most relevant data points are used for model inference, maintaining low latency and adapting to concept drift. This approach avoids the staleness of batch processing and aligns with the continuous nature of streaming pipelines.

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Use batch processing to transform data in fixed intervals

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Store all raw data indefinitely for future analysis

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Retrain the model on a fixed schedule every 24 hours

Question 9

A model trained on customer reviews achieves 98% accuracy on the test set. However, when deployed, it performs poorly on real-world data. The data scientist suspects distribution shift. Which action is MOST important to address this?

Accepted Answer

Implement a monitoring system to detect data drift and retrain with fresh data. Distribution shift (data drift) causes the model's training distribution to differ from the real-world distribution, degrading performance despite high test accuracy. Implementing a monitoring system to detect drift and retraining with fresh data directly addresses this by ensuring the model adapts to the current data distribution, which is the most critical action for maintaining performance in production.

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Reduce the learning rate during training

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Add more features to the model

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Increase the number of cross-validation folds

Question 10

A team of data scientists and engineers is working on multiple AI projects. They often struggle to reproduce experiments and manage model versions. Which tool or practice should they adopt?

Accepted Answer

Use an MLOps platform that provides version control, tracking, and reproducibility.. An MLOps platform (e.g., MLflow, Kubeflow, or Vertex AI) provides integrated version control for code, data, and models, along with experiment tracking and reproducibility. This directly addresses the team's struggle to reproduce experiments and manage model versions by automating lineage capture and enabling consistent environment recreation.

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Document experiments in a shared Word document.

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Share code via email attachments.

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Keep all models in a shared network drive.

Question 11

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 12

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 13

Refer to the exhibit. A security analyst reviews the monitoring log for an AI fraud detection model. Which of the following is the most likely cause of the multiple alerts?

Accepted Answer

An adversarial attack attempt. An adversarial attack attempt is the most likely cause because the monitoring log shows multiple alerts triggered by subtle, crafted perturbations in input data designed to cause the AI fraud detection model to misclassify legitimate transactions as fraudulent or vice versa. Unlike data poisoning, which corrupts the training dataset over time, adversarial attacks target the model's inference phase, exploiting its sensitivity to small input variations to produce incorrect outputs without altering the underlying training data.

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Data poisoning of the training dataset

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A network hardware failure

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A scheduled model retraining process

Question 14

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 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