AI0-001 Practice Test 36 — 15 Questions

Question 1

Which TWO are key differences between Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)?

Accepted Answer

RNNs have internal memory; CNNs do not. RNNs possess a hidden state that acts as internal memory, allowing them to retain information from previous time steps, which is essential for processing sequential data. In contrast, CNNs lack this internal memory mechanism; they process inputs independently without maintaining a state across different inputs, making them unsuitable for tasks requiring temporal context.

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CNNs are designed for sequential data; RNNs for spatial data

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CNNs can handle variable-length inputs; RNNs require fixed-size inputs

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CNNs use backpropagation; RNNs do not

Question 2

A financial institution requires that all AI model predictions be explainable and auditable for regulatory compliance. Which model serving approach should be used to meet these requirements?

Accepted Answer

Deploy the model as a containerised microservice with REST API and log all request/response pairs. Logging all request/response pairs provides a complete audit trail, which is essential for regulatory compliance in financial institutions. Containerized microservices with REST APIs are stateless and can be easily integrated with centralized logging systems (e.g., ELK stack) to capture every prediction for explainability and review. This approach ensures that model decisions are transparent and can be traced back to specific inputs, satisfying both explainability and auditability requirements.

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Use gRPC streaming for lower latency

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Export the model to ONNX format and run on a dedicated inference server

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Deploy the model on edge devices to avoid centralised logging

Question 3

A hospital is implementing an AI system to analyze patient X-rays for potential fractures. The hospital must comply with HIPAA regulations. Which privacy-preserving technique allows the model to be trained on data from multiple hospitals without sharing raw patient data?

Accepted Answer

Federated learning. Federated learning allows multiple hospitals to collaboratively train a model without sharing raw patient data. Each hospital trains a local model on its own data, and only model updates (e.g., gradients or weights) are shared with a central server, which aggregates them. This preserves privacy and helps comply with HIPAA by keeping patient data on-premises.

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Differential privacy

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Data anonymisation

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Data pseudonymisation

Question 4

A company is fine-tuning an LLM for a domain-specific task using LoRA. They have limited GPU memory and need to reduce memory footprint without sacrificing fine-tuning quality. Which approach should they consider?

Accepted Answer

Use QLoRA with 4-bit quantized base model. QLoRA combines 4-bit NormalFloat quantization of the base model with LoRA adapters, drastically reducing GPU memory usage while preserving fine-tuning quality through techniques like double quantization and paged optimizers. This directly addresses the constraint of limited GPU memory without sacrificing the model's ability to learn domain-specific tasks effectively.

Answer

Use a larger batch size to speed up training

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Fine-tune all layers of the base model

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Increase the rank of LoRA adapters

Question 5

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. 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 6

A machine learning engineer is deploying a model to production. Which TWO practices are essential for ensuring reproducibility of model predictions?

Accepted Answer

Version-control the model artifact (e.g., using MLflow or DVC).. Option D is correct because version-controlling the model artifact with tools like MLflow or DVC ensures that the exact trained weights, hyperparameters, and code revision used in production can be retrieved and audited, which is fundamental to reproducing identical predictions. Option E is correct because fixing random seeds across all libraries (e.g., NumPy, TensorFlow, and Python's random module) eliminates nondeterminism in weight initialization, data shuffling, and dropout, so the same inputs yield the same outputs. Option A is not essential for reproducibility since more epochs change the model rather than guarantee deterministic, repeatable predictions. Option B is unnecessary because reproducibility depends on deterministic software and versioned artifacts, not on identical GPU hardware, and inference can run on different accelerators. Option C is also irrelevant because parallel data loading affects throughput and latency, not the determinism or reproducibility of the model's predictions.

Answer

Increase the number of training epochs to ensure convergence.

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Use the same GPU hardware for both training and inference.

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Use parallel data loading to speed up inference.

Question 7

A cybersecurity firm is developing an AI system to detect zero-day malware using behavior analysis. The team collects a dataset of 1,000 malware samples and 10,000 benign files from corporate endpoints. The model is a random forest classifier. After deployment, the false positive rate is 5%, which is acceptable, but the detection rate for new malware variants drops to 30%. The security analyst suspects the model is overfitting to the specific malware families in the training set. Which improvement should the team implement first?

Accepted Answer

Engineer features that capture generic behavioral patterns. The core issue is that the model has overfitted to the specific malware families in the training set, causing poor generalization to unseen zero-day variants. Engineering features that capture generic behavioral patterns (e.g., API call sequences, file system interactions, network connection anomalies) reduces reliance on family-specific signatures, improving detection of novel malware. This directly addresses the root cause of the 30% detection rate drop without introducing new model complexity or data imbalance issues.

Answer

Use a boosting ensemble instead of bagging

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Collect more malware samples from the same families

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Replace the random forest with a deep neural network

Question 8

A machine learning team is developing a model to predict loan defaults using sensitive customer financial data. They need to share the model with third-party auditors without exposing individual customer records. Which privacy-preserving technique allows auditors to query the model while providing mathematical guarantees about the privacy of the training data?

Accepted Answer

Differential privacy. Differential privacy is correct because it adds calibrated noise to the model's training process or query responses, providing a formal mathematical guarantee (ε-differential privacy) that the inclusion or exclusion of any single individual's data does not significantly affect the output. This allows auditors to query the model without exposing individual customer records, as the noise bounds the information leakage from the training data.

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Federated learning

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k-anonymity

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Homomorphic encryption

Question 9

A company wants to build an AI pipeline that processes streaming data from IoT sensors, performs feature engineering, trains a model incrementally, and deploys the updated model. Which data pipeline technology is BEST suited for the streaming ingestion step?

Accepted Answer

Apache Kafka. Apache Kafka is the best choice for the streaming ingestion step because it is a distributed event streaming platform designed for high-throughput, fault-tolerant ingestion of real-time data streams. It acts as a durable message broker that can ingest IoT sensor data in real time and make it available for downstream processing, which aligns perfectly with the requirement for streaming data ingestion.

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Amazon S3

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Apache Spark

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Apache Airflow

Question 10

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with 99% legitimate and 1% fraudulent. Which evaluation metric should be prioritized to assess model performance?

Accepted Answer

F1-score. In a highly imbalanced dataset (99% legitimate, 1% fraudulent), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy without detecting any fraud. The F1-score combines precision and recall into a single metric, making it the preferred choice for evaluating binary classification performance on imbalanced data, as it penalizes both false positives and false negatives equally.

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Accuracy

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Mean Squared Error

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Log Loss

Question 11

An organization's AI system uses a decision tree model for loan approval. The compliance team requires explanations for each decision. Which property of decision trees makes them suitable for this requirement?

Accepted Answer

The decision rules are transparent and can be visualized as a tree. Decision trees inherently provide interpretable decision rules by splitting data based on feature thresholds at each node. The entire model can be visualized as a tree structure, allowing compliance teams to trace the exact path and logic behind each loan approval or rejection, which directly satisfies explainability requirements.

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They can handle nonlinear relationships

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They are robust to outliers

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They can handle missing values

Question 12

A developer wants to integrate an AI-powered text summarization API into their application. They need to authenticate securely and manage usage limits. What is the standard mechanism for authenticating with cloud-based AI services?

Accepted Answer

Include an API key in the HTTP request header. Cloud-based AI services, including text summarization APIs, standardize authentication via API keys passed in the HTTP header (e.g., `Authorization: Bearer ` or `x-api-key: `). This method keeps credentials out of URLs and request bodies, preventing exposure in logs or caches, and aligns with RESTful API best practices and OWASP guidelines for secure API access.

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Provide a username and password in the request body

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Embed the API key in the URL query string

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Use a digital certificate for each request

Question 13

A financial institution uses a regression model to predict credit risk. The model has a high R-squared on training data but low R-squared on test data. Which of the following is the most likely cause?

Accepted Answer

The model is overfitting the training data.. A high R-squared on training data combined with a low R-squared on test data is the classic symptom of overfitting. The model has memorized noise and specific patterns in the training set rather than learning generalizable relationships, causing poor performance on unseen data.

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The features were not standardized before training.

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The model is underfitting the training data.

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There is multicollinearity among the input features.

Question 14

A city government uses an AI system to allocate limited social services resources. To ensure fairness, they want to implement human oversight for high-stakes decisions. Which mechanism allows a human to review and potentially override the AI's decision before it is executed?

Accepted Answer

Human-in-the-loop. Human-in-the-loop (HITL) is the correct mechanism because it requires a human to review and approve or reject the AI's decision before it is executed. This ensures that for high-stakes decisions, such as allocating limited social services, a human can intervene to prevent unfair or erroneous outcomes, directly addressing the fairness requirement.

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Human-on-the-loop

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Human-in-command

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Automated decision-making without human intervention

Question 15

An AI engineer is training a deep neural network for image recognition. The training loss decreases steadily for the first few epochs but then plateaus and starts to oscillate. Which adjustment is most likely to improve convergence?

Accepted Answer

Reduce the learning rate. The plateau and oscillation of the training loss indicate that the optimizer is overshooting the minimum due to a learning rate that is too high. Reducing the learning rate allows the optimizer to take smaller, more precise steps, dampening oscillations and enabling convergence to a lower loss. This is a standard technique in gradient descent optimization, often implemented via learning rate schedules or adaptive methods like Adam.

Answer

Add more layers

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Increase the learning rate

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Increase the batch size