AI0-001 Quick Quiz — 10 Questions

Question 1

Which AI accelerator is specifically designed by Google to accelerate the training and inference of large neural networks, especially in their cloud environment?

Accepted Answer

TPU. The Tensor Processing Unit (TPU) is Google's custom-designed ASIC specifically built to accelerate the training and inference of large neural networks. Unlike general-purpose hardware, TPUs are optimized for TensorFlow workloads and are a core component of Google Cloud's AI infrastructure, offering high throughput for matrix operations common in deep learning.

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GPU

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NPU

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FPGA

Question 2

An AI team is evaluating whether to use AI for a customer segmentation task. They have a dataset of customer demographics and purchase history. Which TWO conditions would make AI a better choice than a traditional rule-based approach? (Select two.)

Accepted Answer

The data contains complex, non-linear patterns that are not easily captured by rules. AI techniques like neural networks or gradient-boosted trees excel at capturing complex, non-linear interactions in high-dimensional data (e.g., purchase sequences combined with demographics) that rule-based systems cannot express without an explosion of brittle, hand-crafted conditions. This makes AI the better choice when the underlying patterns are not linearly separable or easily codified as if-then logic.

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The segmentation criteria are well-understood and can be expressed in simple if-then rules

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The segmentation must be fully explainable to regulators

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The team has no access to labeled data

Question 3

A company uses a machine learning model to recommend products to customers. The marketing team notices that the model is recommending high-profit items more frequently than low-profit items, even when customers are likely to prefer the latter. This behavior is causing customer dissatisfaction. Which approach would best align the model with customer preferences while maintaining profitability?

Accepted Answer

Use a multi-objective optimization framework to balance profit and customer satisfaction.. A multi-objective optimization framework explicitly allows the model to balance multiple goals, such as profit and customer satisfaction, by optimizing both objectives simultaneously. Option A is incorrect because weighting profit more heavily would exacerbate the issue and further ignore customer preferences. Option C is incorrect because adjusting hyperparameters to reduce profit feature influence is not a principled way to balance objectives and may not effectively improve satisfaction. Option D is incorrect because removing profit data entirely ignores legitimate business goals, potentially harming profitability.

Answer

Train the model with a loss function that weights profit more heavily than customer satisfaction.

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Adjust the model's hyperparameters to reduce the influence of profit features.

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Remove profit data from the training set and only use customer preference data.

Question 4

Which TWO of the following are best practices for securing an AI model against adversarial attacks?

Accepted Answer

Adversarial training with perturbed examples.. Option B is correct because adversarial training explicitly augments the training set with perturbed inputs (e.g., FGSM or PGD examples) labeled with their true classes, which hardens the model's decision boundaries against small, intentionally crafted perturbations. Option C is correct because input sanitization and validation filter or normalize anomalous inputs (e.g., clipping pixel ranges, rejecting out-of-distribution values, or detecting unusual feature patterns) before inference, reducing the attack surface for adversarial examples. Option A is not a security best practice for adversarial robustness; pruning reduces parameters for efficiency and can even increase vulnerability by removing redundant features that aid generalization. Option D is wrong because increasing model complexity typically enlarges the attack surface and can worsen overfitting to non-robust features, making adversarial examples easier to find. Option E is irrelevant to adversarial security, as grid search only tunes hyperparameters for performance metrics like accuracy, not robustness against crafted perturbations.

Answer

Model pruning to reduce the number of parameters.

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Increasing model complexity to capture more patterns.

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Hyperparameter optimization using grid search.

Question 5

An AI engineer is tuning a deep learning model and observes that the training loss decreases very slowly. The learning rate is set to 0.001. Which adjustment is most likely to speed up convergence?

Accepted Answer

Increase the learning rate to 0.01. A learning rate of 0.001 is causing the model to take very small steps toward the minimum of the loss function, resulting in slow convergence. Increasing the learning rate to 0.01 allows larger weight updates per iteration, which typically speeds up training. However, care must be taken not to overshoot the optimum, as an excessively high learning rate can cause divergence.

Answer

Add more hidden layers

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Decrease the learning rate to 0.0001

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Increase the batch size

Question 6

A company is deploying a pre-trained image classification model for facial recognition in a security system. They are concerned about adversarial examples. Which TWO of the following are effective defenses against adversarial examples?

Accepted Answer

Adversarial training during model development. Adversarial training during model development (A) is correct because it augments the training set with adversarial examples generated by attacks like FGSM or PGD, so the model learns to classify perturbed inputs correctly and gains genuine robustness. Input sanitization techniques such as JPEG compression or denoising (C) are correct because they destroy or attenuate the small, high-frequency perturbations that adversarial attacks add, reducing the attack's effectiveness before inference. Gradient masking (B) is not a reliable defense: it only obscures gradients and is routinely bypassed by transferability or gradient-free attacks, giving a false sense of security. Homomorphic encryption (D) protects data confidentiality during computation but does not remove or neutralize adversarial perturbations, so it is irrelevant to adversarial robustness. Federated learning (E) addresses privacy and distributed training, not the integrity of predictions against crafted inputs, so it does not defend against adversarial examples.

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Gradient masking to hide model gradients

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Homomorphic encryption of input images

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Federated learning to train on distributed data

Question 7

A data scientist is building a model to predict credit default using historical loan data. The dataset contains 100,000 records with 50 features, including income, debt-to-income ratio, and loan amount. The target variable is binary (default vs. no default). The goal is to maximize interpretability while maintaining high accuracy. Which algorithm is MOST appropriate?

Accepted Answer

Logistic regression. Logistic regression is a linear model that is highly interpretable (coefficients indicate direction and magnitude of feature impact) and performs well on binary classification with many features, especially when the goal is to balance interpretability with accuracy. It avoids the overfitting risk of a single decision tree and the black-box nature of ensembles.

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Random forest

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Gradient boosting machine

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Decision tree

Question 8

A data science team uses Git for version control of model code and DVC for data versioning. They want to implement a model registry to track trained models, their hyperparameters, and performance metrics. Which tool is specifically designed for this purpose and integrates with the existing workflow?

Accepted Answer

MLflow Model Registry. MLflow Model Registry is specifically designed for managing model versions, tracking metadata, and integrating with Git and DVC. Apache Airflow is for workflow orchestration, not model registry. Kubernetes is for container orchestration. Docker is for containerization.

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Apache Airflow

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Docker

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Kubernetes

Question 9

A data science team uses a CI/CD pipeline for ML models. They need to ensure that each model version is traceable back to the exact training data and hyperparameters. Which practice should be implemented?

Accepted Answer

Use a model registry with metadata tracking (e.g., MLflow). A model registry, such as MLflow, serves as a centralized repository that tracks model versions along with metadata like training data snapshots and hyperparameters, ensuring full traceability. Git LFS (Option B) only handles large files, not metadata. Storing artifacts in blob storage with timestamped filenames (Option C) lacks structured tracking and query capabilities. A shared spreadsheet (Option D) is error-prone and not integrated into the CI/CD pipeline.

Answer

Use Git LFS for model files

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Store model artifacts in blob storage with timestamped filenames

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Record hyperparameters in a shared spreadsheet

Question 10

A retail company uses a gradient boosting model to predict customer lifetime value (CLV). The model currently uses 50 features including purchase history, demographics, and web behavior. The model's RMSE on the test set is 120. The data science team wants to improve the model's accuracy without increasing training time significantly. They have access to additional data: customer support interaction logs (text), social media sentiment (text), and third-party credit scores (numeric). They also have the ability to perform feature engineering, hyperparameter tuning, and ensemble methods. Which approach is most likely to yield the best improvement in predictive performance with minimal increase in training time?

Accepted Answer

Engineer new features such as average purchase value and recency. Engineering domain-relevant features like average purchase value and recency directly captures the underlying behavioral patterns that drive customer lifetime value, often providing a higher signal-to-noise ratio than adding raw text or third-party data. This approach leverages existing data without significantly increasing the feature dimensionality or training time, unlike adding TF-IDF vectors which would dramatically expand the feature space and slow training.

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Add the customer support text as a feature using TF-IDF vectors

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Use an ensemble of gradient boosting and random forest models

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Perform hyperparameter tuning using grid search