AI0-001 Multiple Select — 50 Questions

Question 1

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.)

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

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 2

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.

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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 3

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.

Answer

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 4

A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?

Accepted Answer

Revisit training data for historical bias and consider reweighting. Revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.

Answer

Ignore the disparity because the model is accurate overall

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Remove all demographic attributes from the dataset

Question 5

Which THREE are effective methods for ensuring data privacy in AI training? (Choose three.)

Accepted Answer

Data anonymization. Data anonymization (B) is correct because removing or masking personally identifiable information (PII) such as names, addresses, and identifiers before training prevents the model from learning or exposing individual identities. Differential privacy (C) is correct because it adds calibrated statistical noise (e.g., via mechanisms like Laplace or Gaussian noise with a privacy budget epsilon) so that the inclusion or exclusion of any single record has a negligible effect on outputs, providing a formal privacy guarantee. Federated learning (E) is correct because it trains models locally on each device or silo and shares only model updates (e.g., gradients or weights) rather than raw data, keeping sensitive data on the originating endpoint. Data encryption at rest (A) protects stored data against unauthorized access but does not prevent privacy leakage during training or inference, so it is not one of the three methods for ensuring privacy in AI training. Data replication (D) merely copies data to additional locations, which increases exposure and does nothing to protect privacy, so it is not a valid method.

Answer

Data encryption at rest

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Data replication

Question 6

A healthcare AI system is subject to GDPR because it processes patient data. Which THREE requirements must the system satisfy?

Accepted Answer

Right to explanation of decisions. Article 22 of the GDPR grants data subjects the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. For healthcare AI systems, this means patients have the right to obtain an explanation of the decision reached by the algorithm, such as how a diagnosis or treatment recommendation was derived. This requirement ensures transparency and accountability in high-stakes automated decisions.

Answer

Explicit consent from all data subjects

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Data retention period of at least 10 years

Question 7

A deep learning engineer is training a convolutional neural network for image classification. The model is overfitting the training data. Which three techniques can help reduce overfitting? (Choose three.)

Accepted Answer

Add dropout layers. Dropout layers randomly deactivate a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features. This reduces overfitting by acting as a form of ensemble learning without increasing model complexity.

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Use a smaller learning rate

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Increase the number of convolutional layers

Question 8

A data science team wants to implement a feature store to serve pre-computed features for both training and inference with low latency. Which TWO tools are commonly used for building a feature store?

Accepted Answer

Feast. Feast (Feature Store) is an open-source operational data system that manages and serves machine learning features to both training and inference pipelines with low latency. It provides a consistent feature definition API, offline serving for training, and online serving via a low-latency store like Redis or DynamoDB, making it a standard choice for feature store implementations.

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Kubeflow

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Apache Hive

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MLflow

Question 9

A machine learning team is splitting a dataset for a binary classification problem. They want to ensure robust evaluation and avoid data leakage. Which TWO practices should they follow? (Choose 2)

Accepted Answer

Split into training, validation, and test sets. Option B is correct because splitting the dataset into separate training, validation, and test sets allows the model to be tuned on the validation set while the test set remains untouched for an unbiased final evaluation, which is essential for robust assessment of a binary classifier. Option D is correct because shuffling the data before splitting ensures that the training, validation, and test sets are representative of the overall distribution and prevents ordering bias or temporal artifacts from skewing the split, which helps avoid leakage when the data is not inherently ordered. Option A is not correct because normalising the entire dataset before splitting leaks information from the validation and test sets into the training process, since the scaling parameters would be computed using all data. Option C is not correct because including validation data in the training set removes the independent validation signal needed for hyperparameter tuning and model selection, undermining robust evaluation. Option E is not correct because reusing the same split for all experiments can lead to overfitting to that particular split and does not provide the variability needed to assess generalization reliably.

Answer

Normalise the entire dataset before splitting

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Include validation data in the training set for more data

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Use the same split for all experiments

Question 10

A data scientist needs to store large volumes of unstructured log data for future AI model training. They also need to run SQL-based analytics on the data. Which THREE services are appropriate for this requirement? (Choose 3)

Accepted Answer

Snowflake. Snowflake is correct because it is a cloud-native data warehouse that supports both structured and semi-structured data (like JSON, Avro, Parquet) via its VARIANT data type, enabling SQL-based analytics on unstructured log data. It also integrates with cloud storage (e.g., Amazon S3) for storing large volumes of raw logs, making it suitable for AI model training pipelines.

Answer

Pinecone

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pgvector

Question 11

Which THREE techniques can help reduce overfitting in neural networks?

Accepted Answer

Increasing training data size. Increasing the training data size helps reduce overfitting by providing the model with more examples to learn from, which reduces the variance and improves generalization. With more data, the model is less likely to memorize noise and instead learns the underlying patterns, making it more robust on unseen data.

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Using a larger learning rate

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Increasing number of layers

Question 12

A company is deploying a generative AI system that produces text content. To comply with emerging transparency obligations, which THREE measures should they implement?

Accepted Answer

Watermark AI-generated content. Watermarking AI-generated content (Option A) is correct because it embeds an imperceptible, machine-detectable signal into the output, enabling provenance verification. This directly addresses transparency obligations by allowing downstream systems to identify synthetic text, which is a key requirement in emerging AI regulations such as the EU AI Act.

Answer

Encrypt all training data

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Limit model access to internal employees only

Question 13

Which THREE factors are common causes of bias in AI systems?

Accepted Answer

Lack of diversity in the development team. A lack of diversity in the development team leads to homogeneity of thought, which can cause blind spots in identifying potential biases in data, features, or model behavior. When the team does not represent the full spectrum of end users, the AI system may inadvertently encode assumptions that disadvantage underrepresented groups, resulting in biased outcomes.

Answer

Cross-validation

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High regularization

Question 14

A team is deploying an AI model for credit approval. Which TWO ethical considerations must be addressed?

Accepted Answer

Model interpretability. Model interpretability (B) is essential for credit approval because financial decisions must be explainable to regulators and customers under laws like GDPR or ECOA. A black-box model that cannot justify why a loan was denied violates compliance requirements, making interpretability a core ethical and legal necessity.

Answer

Training speed

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Model accuracy

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Model size

Question 15

A retail analytics team is preparing a dataset for a demand forecasting model. The dataset contains a 'store_id' column with several thousand unique values, a 'product_category' column with about twenty values, and a 'day_of_week' column. The team wants to encode these categorical variables so a tree-based model can use them effectively without creating an enormous number of columns. Which TWO encoding approaches are most appropriate? (Choose two.)

Accepted Answer

Use target encoding for 'store_id', replacing each store with a statistic such as the mean of the target computed from training folds.. High-cardinality identifiers like store_id need a compact numeric representation, and out-of-fold target encoding provides one column that captures store-level signal without exploding dimensionality. Low-cardinality nominal features like product_category and day_of_week are best handled with one-hot encoding, which avoids implying an order and keeps the feature space small. Together these choices match encoding strategy to cardinality.

Answer

Use one-hot encoding for 'store_id' so every store gets its own binary column.

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Use binary encoding for 'day_of_week' so each day is represented by a binary code across multiple columns.

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Use label encoding for 'product_category' so categories become arbitrary integers based on alphabetical order.

Question 16

A research lab is training a large language model and wants to minimize its environmental impact. Which THREE practices are most effective for reducing the carbon footprint of model training?

Accepted Answer

Apply model compression techniques like pruning and quantization. Model compression techniques like pruning and quantization directly reduce the computational requirements of training and inference. Pruning removes redundant weights, and quantization reduces the precision of weights (e.g., from 32-bit to 8-bit), which lowers the number of operations and memory bandwidth needed, thereby decreasing energy consumption and carbon emissions.

Answer

Extend the number of training epochs to ensure convergence

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Increase the model size to achieve better accuracy faster

Question 17

A team is building an AI-powered recommendation system for an e-commerce platform. They want to test the system before deployment. Which TWO types of testing are MOST relevant for this AI system? (Select TWO)

Accepted Answer

Evaluation frameworks for model output quality. Option C (Evaluation frameworks for model output quality) is correct because AI/ML systems produce probabilistic outputs that cannot be validated by traditional assertions; frameworks such as accuracy, precision/recall, F1, BLEU, or ROUGE metrics are needed to assess whether the recommendation model generates relevant, high-quality predictions before deployment. Option D (Unit tests for data pipelines) is correct because the recommendation system depends on feature engineering and ETL/data ingestion pipelines; unit tests verify transformations, schema conformance, null handling, and data integrity so that garbage data does not silently degrade model behavior. Option A (Load testing the web server) is not selected because it measures infrastructure throughput and concurrency rather than the AI system's model correctness or data quality. Option B (Integration tests for API calls) is not selected because, while useful generally, it tests service-to-service communication rather than the AI-specific concerns of model output quality and data pipeline correctness. Option E (Regression testing on the UI) is not selected because it validates front-end behavior and visual consistency, which is peripheral to testing the AI model and its data pipeline.

Answer

Load testing the web server

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Integration tests for API calls

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Regression testing on the UI

Question 18

A team is designing a deep learning pipeline for a computer vision task. They want to reduce overfitting. Which two techniques are specifically effective for this purpose? (Select TWO.)

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.

Answer

Using a smaller batch size

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Adding more layers

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Increasing the learning rate

Question 19

A data scientist is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)

Accepted Answer

gamma (kernel coefficient). Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.

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learning rate

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epsilon (for epsilon-SVR)

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degree (for polynomial kernel)

Question 20

A company is adopting a secure development lifecycle for its new AI product. Which THREE activities are essential for secure AI development? (Select three.)

Accepted Answer

Implementing secure data pipelines. Option A (Implementing secure data pipelines) is correct because AI systems depend on large volumes of training and inference data, so protecting data in transit and at rest, validating inputs, and preventing poisoning or leakage require hardened, access-controlled pipelines. Option B (Threat modeling using STRIDE) is correct because STRIDE systematically identifies spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege threats across the AI lifecycle, including model theft, adversarial inputs, and prompt injection. Option D (Audit logging of AI interactions) is correct because immutable logs of prompts, model outputs, data access, and administrative actions provide traceability, support incident response, and satisfy governance and compliance requirements for AI systems. Option C is not essential because deploying on the fastest hardware is a performance optimization, not a security control, and may even increase attack surface or cost without improving security. Option E is not essential because homomorphic encryption for all data at rest is impractical for most AI workloads due to severe performance overhead, and it is not a baseline requirement when standard encryption at rest and in transit already addresses the threat.

Answer

Deploying the model on the fastest hardware available

Answer

Using homomorphic encryption for all data at rest