AI0-001 Practice Test 18 — 15 Questions

Question 1

A global retailer uses an AI model to forecast demand across thousands of stores. After deployment, the model's predictions become less accurate during holiday seasons. The training data included two years of holiday periods. What is the most effective operational strategy to handle this recurring seasonal drift?

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

Accepted Answer

Implement a scheduled retraining cycle just before each holiday period. Scheduled retraining just before each holiday season directly addresses the recurring seasonal drift by updating the model with the most recent holiday data patterns. This is the most effective operational strategy because it proactively aligns the model with the known, periodic shift in demand behavior, rather than reacting to errors or relying on static historical data.

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Deploy an anomaly detection system to flag holiday prediction outliers

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Use an ensemble of models trained on different time periods

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Increase the volume of training data by including five years of history

Question 2

An attacker repeatedly queries a public LLM API with carefully crafted inputs to reconstruct the model's architecture and approximate weights. This is an example of which attack?

Accepted Answer

Model extraction. Model extraction attacks involve querying a public API with carefully crafted inputs to reconstruct a target model's architecture and approximate weights. By analyzing the outputs (e.g., logits or probabilities), an attacker can train a substitute model that mimics the original, enabling offline exploitation or competitive intelligence. This directly matches the scenario described.

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Data poisoning

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Membership inference

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Model inversion

Question 3

An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?

Accepted Answer

Quantization. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.

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Increase batch size

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Add more layers

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Use a larger model

Question 4

A company is deploying an LLM-based chatbot that must output responses in a structured JSON format for downstream processing. Which THREE prompt engineering techniques should the team use to ensure the output is valid and correctly structured? (Select three.)

Accepted Answer

Include few-shot examples of correct JSON outputs. Option A is correct because few-shot examples of correct JSON outputs demonstrate the exact structure, key names, and formatting the model should reproduce, which strongly improves adherence to the desired schema. Option C is correct because enabling JSON mode or structured output mode in the model API constrains generation so the response is syntactically valid JSON, directly preventing malformed output. Option D is correct because defining the expected JSON schema in the system prompt gives the model explicit field names, types, and required structure to follow. Option B is not among the marked correct answers; while low temperature can improve determinism, it does not by itself guarantee valid or correctly structured JSON. Option E is not marked correct because chain-of-thought reasoning improves problem-solving but does not enforce JSON syntax or schema compliance.

Answer

Set temperature to 0 to increase determinism

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Use chain-of-thought prompting to reason before output

Question 5

A company wants to build a system that automatically tags uploaded images with objects they contain (e.g., 'car', 'tree', 'person'). Which AI application type is this?

Accepted Answer

Image classification/object detection. The task of identifying and labeling objects (e.g., 'car', 'tree', 'person') within an image is a classic use case for image classification combined with object detection. Image classification assigns a single label to the entire image, while object detection localizes and classifies multiple objects within the image, which is exactly what the system requires.

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Recommendation system

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Anomaly detection

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Document intelligence

Question 6

A team is training a deep learning model for image classification. The training loss decreases steadily but the validation loss plateaus after 20 epochs and then starts to increase. Which action is MOST likely to improve generalization?

Accepted Answer

Implement early stopping. Early stopping halts training when validation loss stops improving, preventing overfitting. Increasing learning rate would worsen divergence; adding more layers increases capacity and overfitting; reducing batch size may help optimization but not directly address overfitting.

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Add more convolutional layers

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Increase the learning rate

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Reduce the batch size

Question 7

A developer wants to secure an AI API service. Which practice is MOST effective for preventing unauthorized access to the model?

Accepted Answer

Enforcing least-privilege API access with proper key management. Enforcing least-privilege API access with proper key management is the most effective practice because it ensures that each API key or token has only the minimum permissions necessary for its intended function, reducing the attack surface. Proper key management includes rotating keys, using scoped access tokens (e.g., OAuth 2.0 scopes), and storing keys securely (e.g., using a secrets manager like AWS Secrets Manager or HashiCorp Vault). This directly prevents unauthorized access by limiting what a compromised or misused key can do, unlike other options that address secondary concerns.

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Using a larger context window

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Enabling response logging

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Implementing rate limiting

Question 8

An AI system misclassifies rare but critical events. The team considers using synthetic data. Which consideration is MOST important for ensuring the synthetic data improves performance on real rare events?

Accepted Answer

The synthetic data should accurately represent the distribution and features of real rare events.. Synthetic data must faithfully replicate the distribution and feature space of real rare events to enable the model to learn meaningful decision boundaries. If the synthetic data does not capture the true underlying patterns—such as specific sensor readings or transaction anomalies—the model will fail to generalize to actual rare events, defeating the purpose of augmentation.

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The synthetic data should include a wide variety of events, even if not realistic.

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The synthetic data should be generated using an unsupervised generative model.

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The synthetic data should be as large as possible to cover all possibilities.

Question 9

Which embedding type is MOST suitable for capturing semantic meaning of text in a RAG pipeline?

Accepted Answer

Dense embeddings from a pre-trained transformer model. Dense embeddings from pre-trained transformer models (e.g., BERT, Sentence-BERT) capture semantic meaning by representing text as high-dimensional vectors where similar meanings are close in vector space. This is essential for RAG pipelines to retrieve relevant documents based on semantic similarity rather than lexical overlap. Dense embeddings are the standard for modern semantic search.

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Bag-of-words vectors

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TF-IDF vectors

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One-hot encoding

Question 10

During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?

Accepted Answer

Apply oversampling of the minority class using techniques like SMOTE. The dataset is heavily imbalanced (95% negative, 5% positive), which can cause models to be biased toward the majority class. Oversampling the minority class using techniques like SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic examples of the minority class, balancing the class distribution and improving the model's ability to learn the minority class patterns. This is a standard approach to address class imbalance.

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Remove all samples from the majority class to balance the dataset

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Normalize all numerical features to have zero mean and unit variance

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Use a train-test split of 80-20 without any modification

Question 11

You are an AI governance officer at a bank that uses a machine learning model to predict credit risk. The model was developed by an external vendor and uses a proprietary algorithm. The bank's compliance team has determined that the model must be explainable to meet regulatory requirements. However, the vendor claims the model is a 'black box' and cannot provide explanations. You need to ensure compliance while maintaining the model's performance. What is the best course of action?

Accepted Answer

Use a model-agnostic explanation technique like SHAP. D is correct because model-agnostic explanation techniques like SHAP (SHapley Additive exPlanations) can provide post-hoc interpretability for any black-box model without requiring access to its internal structure or proprietary algorithm. This allows the bank to meet regulatory explainability requirements while preserving the vendor's proprietary model and its predictive performance.

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Ignore the requirement as the model is proprietary

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Ask the vendor to develop a custom explanation module

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Replace the model with a simpler, interpretable model

Question 12

An AI developer is selecting a model architecture for a real-time video surveillance system that must detect objects in each frame and also track movement patterns across frames. Which TWO architectures should the developer combine? (Choose 2)

Accepted Answer

Recurrent neural network (RNN) or LSTM. Option E, a convolutional neural network (CNN), is correct because CNNs apply learned spatial filters over pixel grids and are the standard architecture for per-frame object detection and feature extraction in video surveillance, efficiently capturing spatial hierarchies in each image. Option D, an RNN or LSTM, is correct because recurrent architectures model temporal dependencies across sequential frames, allowing the system to learn movement patterns and object trajectories over time; LSTMs in particular mitigate vanishing gradients for longer sequences. Together, a CNN front end for spatial detection plus an RNN/LSTM back end for temporal tracking forms a classic video-analysis pipeline. Option A, a Transformer encoder only, is not the intended pairing here since it lacks the convolutional spatial inductive bias for frame-level detection and, used alone, does not provide the recurrent temporal modeling this scenario calls for. Option B, a GAN, is for generative adversarial training to synthesize or enhance data, not for detection and tracking. Option C, a VAE, is a generative model for learning latent representations and reconstruction, not a supervised detector-tracker component.

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Transformer encoder only

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Generative adversarial network (GAN)

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Variational autoencoder (VAE)

Question 13

Which TWO techniques are commonly used to handle missing data in a machine learning dataset? (Choose TWO.)

Accepted Answer

Imputation with mean or median. Imputation with mean or median is a standard technique for handling missing numerical data because it preserves the dataset size and avoids introducing bias from simply discarding rows. By replacing missing values with the central tendency of the observed data, the model can still learn patterns without losing information, though it may reduce variance slightly.

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Normalization

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One-hot encoding

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Dimensionality reduction

Question 14

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

Accepted Answer

Time-series database. Time-series databases (TSDBs) are optimized for high-ingest rates of timestamped data and provide efficient downsampling, retention policies, and time-based aggregation functions. For IoT sensor streaming, a TSDB like InfluxDB or TimescaleDB delivers sub-second query performance on time-range scans, which is essential for real-time analytics.

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Data warehouse

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Relational database

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Data lake

Question 15

A data engineer needs to store training data in a format that supports columnar pruning during model training. Which storage format should they use?

Accepted Answer

Parquet. Parquet is the correct choice because it is a columnar storage format that enables column pruning, allowing the training process to read only the columns needed for model training rather than entire rows. This reduces I/O and speeds up data loading, which is critical for large-scale AI/ML workloads. Unlike row-oriented formats, Parquet stores data by columns, making it efficient for analytical queries and feature selection.

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XML

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JSON

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CSV