AI0-001 Practice Exam 54 — 20 Questions

Question 1

An AI team wants to version control datasets, track experiments, and log model parameters across multiple projects. Which MLOps platform is specifically designed for experiment tracking and model management?

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

Accepted Answer

MLflow. MLflow is an open-source MLOps platform specifically designed for experiment tracking, model management, and reproducibility. It provides a unified API to log parameters, metrics, and artifacts across multiple projects, making it the correct choice for versioning datasets, tracking experiments, and managing models.

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SageMaker Pipelines

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Vertex AI Pipelines

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Kubeflow

Question 2

A company wants to use AI to analyze customer reviews and determine sentiment (positive, negative, neutral). Which AI subfield is most directly applicable?

Accepted Answer

Natural language processing. Natural language processing (NLP) is the AI subfield that enables machines to understand, interpret, and generate human language. Analyzing customer reviews for sentiment requires processing text, extracting meaning, and classifying it as positive, negative, or neutral, which is a core NLP task called sentiment analysis.

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Reinforcement learning

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Computer vision

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Robotics

Question 3

A CI/CD pipeline for a computer vision model uses canary deployment. After deploying a new version to 5% of traffic, the pipeline automatically rolls back due to a spike in error rate. The new model's inference time is 20% higher than the previous version. The operations team finds that the error is caused by timeout in the inference service. Which action should be taken to prevent future rollbacks?

Accepted Answer

Optimize the model using TensorRT or ONNX Runtime before deployment. The root cause of the timeout is the 20% higher inference time of the new model. Optimizing the model using TensorRT or ONNX Runtime reduces inference latency directly, addressing the performance bottleneck that causes timeouts. This prevents the spike in error rate and subsequent rollback without masking the underlying issue.

Answer

Increase the timeout threshold for inference requests

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Implement a fallback to the previous model when timeout occurs

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Reduce the canary percentage to 1% to minimize impact

Question 4

An organization deploys an AI model on edge devices for real-time image classification. Which metric is most important to monitor for ensuring the device's operational health?

Accepted Answer

Inference memory consumption. For edge devices with limited resources, inference memory consumption is the most critical operational health metric because exceeding available memory can cause the model to crash or the device to become unresponsive. Unlike accuracy or confidence, memory usage directly reflects whether the device can sustain real-time inference without resource exhaustion.

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Model calibration error

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Average prediction confidence

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Model accuracy on local test data

Question 5

A security engineer is implementing defenses against membership inference attacks on a classification model. Which TWO techniques are most effective? (Select TWO.)

Accepted Answer

Differential privacy. Option C (Differential privacy) is correct because it bounds the influence any single training record can have on the model's output by adding calibrated noise (e.g., via DP-SGD with a privacy budget ε), which directly limits the confidence signal an attacker can exploit to infer whether a specific individual was in the training set. Option E (Model regularization) is correct because techniques such as L2 weight decay, dropout, and early stopping reduce overfitting, and overfitting is the primary cause of the train-test performance gap that membership inference attacks detect. Option A (Data augmentation) is not among the marked answers; while it can reduce overfitting incidentally, it does not provide a formal privacy guarantee against membership inference. Option B (Homomorphic encryption) protects data during computation but does not prevent inference about training-set membership from model outputs. Option D (Increasing model size) is counterproductive, as larger models tend to overfit more and thus become more vulnerable to membership inference.

Answer

Data augmentation

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Homomorphic encryption

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Increasing model size

Question 6

A financial institution uses a deep learning model for fraud detection. The model is a feedforward neural network with three hidden layers. It was trained on a balanced dataset of 100,000 transactions. During deployment, the model achieves high accuracy on the test set but the fraud detection rate (true positive rate) is only 40% while the false positive rate is 0.1%. The business requires a true positive rate of at least 80%. Which of the following actions is most likely to achieve the required true positive rate while minimizing the increase in false positives?

Accepted Answer

Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value. (increase hidden layers) may capture more complexity but does not directly increase TPR and could overfit. Option B (SMOTE) rebalances the training set, but the dataset is already balanced, so this is unlikely to improve TPR. Option D (L2 regularization) reduces overfitting but increases bias, which could lower TPR. Option C (change threshold) is the most direct approach: lowering the classification threshold increases the true positive rate, and by tuning, it can achieve 80% TPR with a minimal increase in false positives.

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Increase the number of hidden layers to five to capture more complex patterns

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Use synthetic minority oversampling (SMOTE) to rebalance the training set

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Add L2 regularization to reduce overfitting

Question 7

Based on the exhibit, what is the likely problem with the model?

Accepted Answer

Overfitting. The exhibit shows training loss decreasing to near zero while validation loss increases after a certain point, which is a classic sign of overfitting. The model is memorizing the training data rather than learning generalizable patterns, leading to poor performance on unseen data.

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Batch size too small

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Learning rate too high

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Underfitting

Question 8

A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?

Accepted Answer

F1-score. When the positive class is rare (e.g., 1% of total data), accuracy is misleading because a classifier that always predicts the negative class would achieve 99% accuracy. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by focusing on the positive class performance. It is the most appropriate metric for evaluating binary classifiers on imbalanced datasets.

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True negative rate

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Mean squared error

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Accuracy

Question 9

A team is implementing a document intelligence solution to extract key-value pairs from invoices. They plan to use a pre-trained vision-language model with a RAG pipeline that indexes invoice images. Which chunking strategy is BEST suited for invoice documents that have a consistent layout but vary in length?

Accepted Answer

Hierarchical chunking that groups lines into logical sections (header, line items, totals). Invoices typically have sections (header, line items, totals). Hierarchical chunking preserves this structure, enabling retrieval at the section level. Fixed-size may split important fields, semantic chunking is less predictable on structured documents.

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Semantic chunking based on sentence boundaries

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Fixed-size chunking with 512 tokens per chunk

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No chunking; pass the entire invoice as one document per query

Question 10

Which TWO techniques are commonly used to handle missing data in a dataset?

Accepted Answer

Remove rows with missing values. Removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.

Answer

Feature scaling

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One-hot encoding

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Principal component analysis (PCA)

Question 11

A machine learning engineer notices that the gradient values in a deep network are becoming extremely small during backpropagation. What is this problem?

Accepted Answer

Vanishing gradient. The vanishing gradient problem occurs when gradients become extremely small during backpropagation, especially in deep networks with many layers. This causes the weights in earlier layers to update very slowly or not at all, severely hindering training. The correct answer is D because the scenario directly describes the hallmark symptom of vanishing gradients.

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Dead ReLU

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Exploding gradient

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Covariate shift

Question 12

An organization is implementing an AI-powered chatbot for customer service. The chatbot must comply with GDPR and handle data subject access requests (DSARs). Which design approach best ensures compliance?

Accepted Answer

Implement an audit trail that logs interactions with a unique user identifier, and provide a mechanism to delete logs upon user request.. GDPR requires that personal data be stored only as long as necessary and that data subjects have the right to erasure. By logging interactions with a unique user identifier and providing a deletion mechanism, the chatbot can fulfill DSARs while maintaining an audit trail for compliance monitoring. This approach balances operational needs with regulatory obligations.

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Minimize data collection by not logging any user interactions.

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Anonymize all user data before logging interactions.

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Encrypt all chat logs and store them indefinitely for audit purposes.

Question 13

A team is deploying a machine learning model on a Kubernetes cluster. They need to ensure low-latency inference and efficient resource utilization. Which approach should they use to dynamically scale inference pods based on request volume?

Accepted Answer

Use a Horizontal Pod Autoscaler (HPA) with target CPU utilization. The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically scales the number of inference pods based on observed CPU utilization or custom metrics, ensuring low-latency inference by adding replicas during traffic spikes and reducing waste during idle periods. This dynamic scaling aligns with the need for efficient resource utilization in a Kubernetes cluster, as it adjusts pod count in real-time to match request volume without manual intervention.

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Use a Job resource to process requests in batch

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Deploy a single large pod on a powerful node

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Set a fixed number of pod replicas equal to the maximum expected load

Question 14

Which hardware accelerator is specifically designed by Google for training and inference of machine learning models, particularly their TensorFlow framework?

Accepted Answer

TPU. TPU (Tensor Processing Unit) is Google's custom ASIC designed to accelerate ML workloads, especially with TensorFlow.

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NPU

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FPGA

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GPU

Question 15

A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)

Accepted Answer

Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.. Cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.

Answer

Reduce the number of features using principal component analysis (PCA).

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Use accuracy as the primary evaluation metric.

Question 16

A security team is red teaming an LLM-powered application. Which activity is MOST likely to be performed during red teaming?

Accepted Answer

Attempting jailbreaks to bypass safety guardrails. Red teaming an LLM-powered application focuses on adversarial testing to uncover security vulnerabilities, not on evaluating model performance or data quality. Attempting jailbreaks directly tests whether the LLM's safety guardrails can be bypassed to produce harmful or restricted outputs, which is the core objective of red teaming in AI security.

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Calculating the model's accuracy on a test set

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Reviewing the model's training data for bias

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Auditing the model's inference latency

Question 17

A company is building a secure AI system that must comply with GDPR. They want to allow users to request deletion of their personal data from training sets and model outputs. Which THREE techniques should they implement?

Accepted Answer

Differential privacy. Differential privacy (B) is correct because it adds calibrated noise to training data or model outputs, ensuring that the inclusion or exclusion of any individual's data does not significantly affect the model's behavior. This provides a mathematical guarantee of privacy, which is essential for GDPR compliance when handling personal data. By limiting information leakage, differential privacy helps protect user data even if deletion requests are not fully implemented.

Answer

Model ensembling

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Federated learning

Question 18

A team is building a retrieval-augmented generation (RAG) pipeline. They need to store embeddings of company documents and perform fast similarity searches. Which data store is BEST suited for this task?

Accepted Answer

Pinecone. Pinecone is a purpose-built vector database designed for storing and querying high-dimensional embeddings with fast approximate nearest neighbor (ANN) search. In a RAG pipeline, embeddings of company documents must be retrieved quickly to feed relevant context to the LLM, and Pinecone’s optimized indexing (e.g., HNSW or IVF) and serverless scaling make it the ideal choice for this task.

Answer

Snowflake

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Apache Kafka

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Amazon S3

Question 19

Which THREE are common machine learning algorithms used for regression?

Accepted Answer

Linear regression. Linear regression (C) is the canonical regression algorithm, modeling a continuous target as a linear combination of input features by minimizing squared error. Decision tree (D) regressors recursively split data on feature thresholds to predict continuous values, e.g., via variance reduction or mean-squared-error minimization at each node. K-nearest neighbors (E) performs regression by averaging the target values of the k closest training points under a chosen distance metric, making it a standard non-parametric regressor. Logistic regression (A) is excluded because it is a classification algorithm that outputs class probabilities via the sigmoid/softmax function, not a continuous value. K-means (B) is excluded because it is an unsupervised clustering algorithm that partitions data into groups, not a supervised regression method.

Answer

Logistic regression

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K-means

Question 20

A data scientist is using SHAP to explain a complex ensemble model's predictions. A business stakeholder asks why a particular prediction was made. The data scientist wants to show the most influential features for that single prediction. Which SHAP visualisation is most appropriate?

Accepted Answer

A SHAP force plot for the individual prediction. A SHAP force plot is specifically designed to visualize the contribution of each feature to a single prediction, showing how features push the prediction from the base value (average model output) to the final prediction. This makes it the ideal choice for explaining an individual prediction to a business stakeholder, as it provides a clear, localized explanation of feature impacts.

Answer

A SHAP summary plot showing mean absolute SHAP values across all features

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A SHAP dependence plot for the top feature

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A SHAP bar chart of absolute feature importance