AI0-001 Practice Test 13 — 15 Questions

Question 1

Refer to the exhibit. A machine learning pipeline configuration is shown. During a deployment, the model evaluation passes with accuracy 0.86 and precision 0.79. However, the pipeline proceeds to deploy. What is the most likely reason for this behavior?

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

Accepted Answer

The deployment only checks the accuracy threshold for rollback condition. The pipeline configuration shows a rollback condition that only checks the accuracy metric (accuracy < 0.85). Since the model achieved accuracy 0.86, which is above the threshold, the condition is not triggered, and the pipeline proceeds to deploy regardless of the precision value. The precision metric is not part of the rollback evaluation logic in this configuration.

Answer

The precision metric is not included in the evaluation script

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The deployment target is set to staging instead of production

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The operator manually overrode the threshold

Question 2

An e-commerce company uses a machine learning model to recommend products to users. The model is retrained weekly and deployed to production. For the past three weeks, the model's click-through rate (CTR) has been stable except on Mondays, when it drops by 15%. Analysis reveals that the training data is extracted on Sundays and includes only weekday behavior. On Mondays, user behavior shifts due to weekend browsing patterns not captured in the training data. The team wants to maintain a weekly retraining cadence but fix the Monday performance drop. Which solution best addresses the Monday CTR drop without changing the retraining frequency?

Accepted Answer

Modify the data pipeline to include the full week (including the past weekend) in each retraining. It directly addresses the root cause: the training data excludes weekend behavior, causing the model to be blind to Monday patterns. By modifying the data pipeline to include the full week (including the past weekend) in each retraining, the model learns from weekend browsing patterns and can generalize to Monday user behavior without changing the weekly retraining cadence. This ensures the training distribution matches the inference distribution on Mondays, stabilizing CTR.

Answer

Deploy a separate model specifically for Monday predictions

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Serve the previous week's model on Mondays to use older but stable patterns

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Change to daily retraining to include weekend data more promptly

Question 3

Which neural network architecture is specifically designed to handle sequential data and mitigate the vanishing gradient problem?

Accepted Answer

Long Short-Term Memory (LSTM) network. LSTM (Long Short-Term Memory) is a type of RNN designed with gating mechanisms to prevent vanishing gradients in long sequences. CNNs are for spatial data; vanilla RNNs suffer from vanishing gradients; transformers use attention but are not specifically designed to mitigate vanishing gradients (they use residual connections).

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Convolutional Neural Network (CNN)

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Transformer

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Vanilla Recurrent Neural Network (RNN)

Question 4

A team wants to predict monthly sales using historical data. Which algorithm is most appropriate?

Accepted Answer

Linear regression. Linear regression is the most appropriate algorithm because the goal is to predict a continuous numerical value (monthly sales) based on historical data. It models the relationship between input features and the target variable by fitting a linear equation, making it ideal for regression tasks where the output is a real number.

Answer

K-means

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Decision tree

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Logistic regression

Question 5

An AI risk manager is applying the NIST AI Risk Management Framework (AI RMF). In which function would the organization establish a risk management process and assign roles and responsibilities for AI oversight?

Accepted Answer

Govern. The Govern function in the NIST AI RMF is specifically designed to establish organizational structures, policies, and accountability mechanisms for AI risk management. This includes defining roles and responsibilities, setting risk management processes, and ensuring oversight across the AI lifecycle. The other functions (Map, Measure, Manage) focus on different aspects such as understanding context, assessing risks, and treating risks, respectively.

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Map

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Manage

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Measure

Question 6

A healthcare AI startup is developing a model to predict patient readmission risk. The company wants to ensure the model's decisions can be understood by clinicians. Which explainability technique provides local, model-agnostic explanations by fitting a simple surrogate model around a prediction?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct technique because it generates local explanations by fitting a simple, interpretable surrogate model (e.g., linear regression or decision tree) around a specific prediction. It is model-agnostic, meaning it works with any black-box classifier, and it perturbs the input data near the instance of interest to understand which features most influenced the prediction.

Answer

SHAP values

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Attention visualisation

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Model cards

Question 7

Which THREE factors are most critical to consider when designing a continuous integration/continuous deployment (CI/CD) pipeline for machine learning?

Accepted Answer

Data quality and schema validation. Option A (Data quality and schema validation) is critical because ML pipelines depend on input data distributions and formats; without validating schema, ranges, and drift, training and inference can silently break or degrade. Option C (Automated model performance benchmarking) is essential because a CI/CD pipeline for ML must gate deployments on metrics such as accuracy, F1, RMSE, or latency against a baseline, not just on code tests. Option E (Versioning of datasets, models, and training code) is required for reproducibility and rollback, since ML artifacts are non-deterministic and must be traceable across data, code, hyperparameters, and model binaries. Option B is useful for post-deployment experimentation but is not one of the three most critical pipeline design factors, and Option D, while important for general software CI, is insufficient for ML-specific concerns like data validation, model metrics, and artifact lineage.

Answer

A/B testing framework for comparing models

Answer

Automated unit testing of application code

Question 8

An ML team deploys a model on edge devices using INT8 quantization. They notice a significant drop in accuracy on a subset of classes. Which technique should they apply to recover accuracy without increasing model size?

Accepted Answer

Apply quantization-aware training (QAT). Quantization-aware training (QAT) simulates INT8 quantization effects during the forward pass of training, allowing the model to learn weights and activations that are more robust to the lower precision. This recovers accuracy lost during post-training quantization without increasing the model's size, as the architecture and number of parameters remain unchanged.

Answer

Use pruning to remove less important weights

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Increase the model architecture size

Answer

Switch to FP16 quantization

Question 9

A data scientist is preparing a dataset for a binary classification model. The dataset has 95% majority class and 5% minority class. Which data preparation technique is BEST to address the class imbalance?

Accepted Answer

SMOTE oversampling of the minority class. SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates new, synthetic minority class samples by interpolating between existing minority instances and their nearest neighbors, rather than simply duplicating them. This directly addresses severe class imbalance (95:5) by enriching the minority class without discarding valuable majority data. Unlike random oversampling, SMOTE reduces the risk of overfitting to exact copies and helps the model learn a more generalizable decision boundary.

Answer

Min-max normalization of all features

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Random undersampling of the majority class

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Removing all minority class samples

Question 10

A retail company deploys a machine learning model to predict customer churn. The model outputs a probability between 0 and 1, and churn is predicted if probability > 0.5. After deployment, the model has a high false positive rate (many non-churning customers labeled as churn), which leads to unnecessary retention offers and increased costs. The data science team confirms the model was trained on historical data with a balanced class distribution. The business team wants to reduce false positives while maintaining a reasonable true positive rate. However, they cannot retrain the model because the original training data is no longer available. What is the best course of action to reduce false positives?

Accepted Answer

Increase the decision threshold to a higher value, such as 0.7.. Increasing the decision threshold to a higher value, such as 0.7, reduces false positives because the model will only predict churn when it is more confident. Since the model cannot be retrained, adjusting the threshold is the only way to trade off between precision and recall without modifying the model itself.

Answer

Retrain the model using only the most recent three months of data.

Answer

Collect new labeled data and perform transfer learning from the original model.

Answer

Decrease the decision threshold to a lower value, such as 0.3.

Question 11

A company is considering using an open-source large language model for a commercial application. Which intellectual property consideration is MOST important when deciding between open-source and proprietary models?

Accepted Answer

The model's license terms and any restrictions on commercial use. The license terms of an open-source model dictate whether and how it can be used commercially, modified, or redistributed. Some licenses (e.g., Apache 2.0, MIT) are permissive, while others (e.g., GPL, AGPL, or custom licenses like Llama 2's) impose restrictions such as copyleft, attribution, or limits on commercial use. For a commercial application, failing to comply with these terms can lead to legal liability, making license review the most critical IP consideration.

Answer

The model's accuracy on benchmark tasks

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The size of the model's parameter count

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The model's training data provenance

Question 12

A machine learning engineer is building a recommendation system for an e-commerce platform. The system should suggest products based on user purchase history and browsing behavior. Which model selection is BEST suited for this task?

Accepted Answer

Collaborative filtering model (e.g., matrix factorization). Collaborative filtering models (e.g., matrix factorization) are effective for recommendation tasks using user-item interaction data. Linear regression is for regression, not recommendation. Image classification is unrelated. Random forests can be used but are less common for collaborative filtering.

Answer

Image classification model (e.g., CNN)

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Linear regression

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Random forest classifier

Question 13

A data scientist is choosing a hardware accelerator for training a large transformer model. Which of the following is specifically designed for deep learning workloads and offers the highest throughput for matrix multiplications?

Accepted Answer

TPU. The TPU (Tensor Processing Unit) is an application-specific integrated circuit (ASIC) designed by Google specifically to accelerate deep learning workloads. Its systolic array architecture is optimized for the matrix multiplications and convolutions that dominate transformer model training, delivering the highest throughput among the listed options for these operations.

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GPU

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NPU

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CPU

Question 14

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.

Answer

Transformer

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Generative adversarial network (GAN)

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Recurrent neural network (RNN)

Question 15

A data scientist is training a random forest model on a large dataset and notices that the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

Accepted Answer

Decrease the maximum depth of trees. Decreasing the maximum depth of trees limits how deep each decision tree can grow, which reduces the model's capacity to learn overly specific patterns from the training data. This directly combats overfitting by enforcing simpler trees that generalize better to unseen data.

Answer

Increase the maximum features

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Decrease the minimum samples split

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Increase the number of trees