AI0-001 Practice Test 4 — 25 Questions

Question 1

A data scientist wants to group customers into segments based on purchasing behavior without predefined labels. Which type of machine learning is most appropriate?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no predefined labels and wants to discover natural groupings in customer purchasing behavior. Clustering algorithms, such as K-means or DBSCAN, are used in unsupervised learning to segment data based on inherent patterns without any target variable.

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Reinforcement learning

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Supervised learning

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Semi-supervised learning

Question 2

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

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Add more dropout layers

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Use a larger batch size

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Increase learning rate

Question 3

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 4

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

Accepted Answer

Implement early stopping based on validation loss to prevent further overfitting.. The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.

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Reduce the batch size to introduce more noise during training.

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Increase the learning rate to help the model escape the plateau.

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Add more convolutional layers to increase model capacity.

Question 5

A data scientist is deploying a machine learning model to production. The model was trained on an imbalanced dataset. Which technique should be used during deployment to mitigate bias without retraining the model?

Accepted Answer

Apply post-processing calibration to adjust decision thresholds. Post-processing calibration adjusts the decision threshold of the model to account for the class imbalance present in the training data. This technique modifies the output probabilities or classification boundary without requiring access to the original training data or retraining the model, making it suitable for deployment scenarios where the model is already fixed.

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Use an ensemble of models trained on balanced subsets

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Rebalance the dataset using SMOTE before inference

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Remove sensitive features from the input data

Question 6

A company wants to roll out a new recommendation model to production. They decide to run an A/B test where 10% of users see the new model and 90% see the old model. After one week, the new model shows a 5% improvement in click-through rate. What is the next best action?

Accepted Answer

Increase the testing percentage gradually while monitoring performance metrics and guardrails. A 5% improvement observed over only one week with a 10% traffic split is insufficient to confirm statistical significance or rule out novelty effects, data drift, or seasonal bias. The recommended best practice in AI deployment is to gradually increase the testing percentage (e.g., 10% → 25% → 50% → 100%) while continuously monitoring performance metrics and guardrails (e.g., click-through rate, conversion rate, latency, and error rates) to ensure the new model generalizes safely across the full user population.

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Immediately roll out the new model to 100% of users

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Revert to the old model because the improvement is minimal

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Run the test for another month to ensure statistical significance

Question 7

A financial institution deploys an AI credit scoring model. After six months, the model's performance drops significantly. Analysis shows that the relationship between features and labels has changed. Which term describes this phenomenon?

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target label changes over time, which is exactly what happened when the credit scoring model's performance dropped due to a shift in the feature-label relationship. This is distinct from data drift, which only involves changes in the input data distribution without affecting the label mapping.

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Model decay

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Overfitting

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Data drift

Question 8

Refer to the exhibit. A machine learning pipeline configuration is shown. During a deployment, the model evaluation passes with accuracy 0.86 and precision 0.79. However, the pipeline proceeds to deploy. What is the most likely reason for this behavior?

Accepted Answer

The deployment only checks the accuracy threshold for rollback condition. The pipeline configuration shows a rollback condition that only checks the accuracy metric (accuracy < 0.85). Since the model achieved accuracy 0.86, which is above the threshold, the condition is not triggered, and the pipeline proceeds to deploy regardless of the precision value. The precision metric is not part of the rollback evaluation logic in this configuration.

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The precision metric is not included in the evaluation script

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The deployment target is set to staging instead of production

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The operator manually overrode the threshold

Question 9

A company streams sensor data from IoT devices. The data arrives as JSON messages at high velocity. Which data pipeline architecture is BEST suited to handle this streaming data for near-real-time analytics?

Accepted Answer

Stream processing using Apache Kafka and Spark Streaming.. Apache Kafka acts as a distributed, fault-tolerant ingestion layer that can handle high-velocity JSON messages, while Spark Streaming processes the data in micro-batches for near-real-time analytics. This combination provides the low-latency, scalable pipeline required for streaming IoT sensor data, unlike batch or single-node approaches.

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Batch processing using Hadoop MapReduce every 24 hours.

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Batch processing using nightly ETL jobs.

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Single-node database with periodic inserts.

Question 10

A data engineer needs to combine two datasets, each with unique customer_id, to include all records from both datasets. Which join type should be used?

Accepted Answer

FULL OUTER JOIN. A FULL OUTER JOIN returns all records from both datasets, matching rows where the customer_id is present in both and filling in NULLs for missing matches. This is the only join type that guarantees every unique customer_id from either dataset appears in the result, which is exactly what the requirement specifies.

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RIGHT JOIN

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LEFT JOIN

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INNER JOIN

Question 11

A team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?

Accepted Answer

Log transformation. Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.

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Principal component analysis (PCA)

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Standardization (Z-score)

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One-hot encoding

Question 12

Refer to the exhibit. A stream processor ingests events. One event arrives with missing "user_id". What will happen?

Accepted Answer

The event will be rejected because user_id is required.. In stream processing systems like Apache Kafka or AWS Kinesis, if a required field such as 'user_id' is missing and the schema (e.g., Avro, JSON Schema) defines it as required, the event will be rejected at ingestion. The stream processor typically validates the event against the schema; if validation fails, the event is not accepted into the stream, preventing downstream processing errors.

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The event will be stored in a dead-letter queue automatically.

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The event will be accepted but user_id will be set to null.

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The event will be accepted with a default user_id of 0.

Question 13

During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?

Accepted Answer

Multicollinearity. Creating a new feature as a linear combination of two existing features introduces perfect multicollinearity, where the new feature is an exact linear function of the original ones. This violates the assumption of no perfect multicollinearity in linear models, causing the design matrix to become singular and making coefficient estimates unstable or impossible to compute. Even in non-linear models, high multicollinearity can inflate variance and reduce interpretability.

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Data leakage

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Overfitting

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Underfitting

Question 14

A healthcare provider wants to use AI to predict patient readmission risk. They have structured data (age, diagnosis, lab results) and unstructured clinical notes. Which approach is most appropriate?

Accepted Answer

Multimodal model combining structured and text embeddings. The scenario involves both structured data (age, diagnosis, lab results) and unstructured clinical notes. A multimodal model can process both types by combining embeddings from text (e.g., via a transformer or RNN) with structured features, enabling the model to learn cross-modal patterns that improve readmission risk prediction. This approach leverages the complementary strengths of structured and unstructured data, which is essential for capturing the full clinical picture.

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Convolutional neural network (CNN) on clinical notes

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Recurrent neural network (RNN) on structured data

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Logistic regression on structured data only

Question 15

A computer vision team is building an image classifier for rare wildlife species. The dataset has only 500 images per class, and the model overfits. Which THREE data augmentation techniques are most likely to reduce overfitting? (Choose three.)

Accepted Answer

Horizontal flip. Horizontal flip is a simple and effective data augmentation technique that doubles the training data by mirroring images, which helps the model generalize better to variations in orientation. This is particularly useful for wildlife images where the animal may appear facing left or right, reducing overfitting by exposing the model to more diverse examples without collecting new data.

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Adding Gaussian noise

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Random cropping

Question 16

A real-time recommendation system requires low latency. Which data storage strategy is best for serving user profiles and item embeddings?

Accepted Answer

In-memory key-value store (e.g., Redis). An in-memory key-value store like Redis is ideal for serving user profiles and item embeddings in a real-time recommendation system because it provides sub-millisecond read/write latency by keeping data in RAM. This directly meets the low-latency requirement for fetching embeddings and profiles on each request, without the overhead of disk I/O or complex query processing.

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Time-series database (e.g., InfluxDB)

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Relational database with joins

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Data lake in object storage (e.g., Amazon S3)

Question 17

An ML team uses Kubeflow to orchestrate a pipeline that includes data preprocessing, model training, and evaluation. The pipeline runs on a Kubernetes cluster. After a cluster upgrade, the pipeline fails at the training step with an 'OOMKilled' error. What is the MOST likely cause?

Accepted Answer

The Kubernetes node's memory resources were not correctly allocated to the pod's resource requests or limits. OOMKilled indicates the container exceeded its memory limit. The resource requests/limits likely were not adjusted for the new cluster configuration, or the node's allocatable memory decreased after upgrade.

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The training code has a memory leak

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The pipeline definition is missing a step dependency

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The training data is corrupted

Question 18

Refer to the exhibit. An AI auditor reviews the fairness configuration. What is the purpose of this policy?

Accepted Answer

Ensure equal positive prediction rates across groups. The policy sets a fairness constraint that requires the model's positive prediction rate (the fraction of instances predicted as the positive class) to be equal across all defined groups. This is a standard demographic parity requirement, which is implemented by adjusting the decision threshold or reweighting training data to ensure that each group receives the same proportion of positive predictions, regardless of the actual outcome distribution.

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Ensure equal error rates across groups

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Ensure equal accuracy across groups

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Ensure model interpretability

Question 19

A company operating in the EU must comply with GDPR. An AI model processes personal data for customer segmentation. Which of the following ensures compliance?

Accepted Answer

Implement data anonymization and allow users to request deletion.. GDPR mandates that personal data must be processed lawfully, with data minimization and the right to erasure. Implementing data anonymization removes personally identifiable information (PII) so the data is no longer considered personal data under GDPR, and allowing users to request deletion directly satisfies the 'right to be forgotten' (Article 17). This approach ensures compliance by both protecting individual privacy and providing a mechanism for data subjects to exercise their legal rights.

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Obtain explicit consent once and use data indefinitely.

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Store personal data permanently for model improvement.

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Use only aggregated data without any individual records.

Question 20

An AI system that can perform any intellectual task that a human being can is referred to as:

Accepted Answer

Artificial General Intelligence (AGI). Artificial General Intelligence (AGI) is the concept of a machine that understands or learns any intellectual task that a human being can.

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Machine learning

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Narrow AI

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Deep learning