AI0-001 Practice Test 47 — 15 Questions

Question 1

A team is deploying a BERT-based question-answering model using a REST API endpoint with gRPC for internal microservices. They notice high latency for small payloads. Which optimization is MOST likely to reduce latency?

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

Accepted Answer

Enable batching of multiple queries into a single request. Batching multiple queries into a single request reduces the overhead of repeated gRPC connection setup, serialization, and network round trips for small payloads. This amortizes the fixed cost of each inference call across several queries, directly lowering per-query latency in high-throughput scenarios.

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Convert the model to ONNX and use ONNX Runtime

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Switch from gRPC to REST with HTTP/2

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Use a larger instance type with more CPU

Question 2

Which TWO of the following are techniques used for reducing overfitting in neural networks? (Choose 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.

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Boosting

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Increasing the learning rate

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Increasing the number of hidden layers

Question 3

A data engineer discovers that a dataset contains duplicate rows. Which data cleaning step is MOST appropriate?

Accepted Answer

Remove duplicate rows.. Removing duplicate rows is the most appropriate data cleaning step because duplicate rows can bias statistical analyses and machine learning models by overrepresenting certain observations. In data engineering, deduplication is a standard preprocessing step to ensure data integrity and avoid skewed results. Option C directly addresses this by eliminating redundant entries without introducing artificial values or altering the data distribution.

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Keep only the first occurrence.

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Fill duplicates with the mean.

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Convert duplicates to categorical.

Question 4

An organization is developing an AI system to approve loan applications. They want to ensure the model does not discriminate based on race or gender. Which technique BEST addresses this concern?

Accepted Answer

Apply adversarial debiasing during model training.. Adversarial debiasing is a technique that explicitly trains the model to remove sensitive information (like race or gender) from its internal representations, preventing the model from learning discriminatory patterns even if correlated features remain. This directly addresses fairness by making the model's predictions independent of protected attributes, which is more robust than simply removing features (which can still allow proxy discrimination).

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Remove race and gender features from the training data.

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Use a more complex model to capture nuances.

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Collect more training data from diverse populations.

Question 5

A bank uses an AI model to approve loans. During an audit, it is found that the model denies loans at a higher rate for a certain ethnic group. Which governance principle is primarily violated?

Accepted Answer

Fairness. The model's disparate impact on a specific ethnic group directly violates the principle of Fairness, which requires that AI systems do not discriminate based on protected attributes such as race, ethnicity, or gender. In lending, fairness is often assessed using metrics like demographic parity or equal opportunity, and a higher denial rate for one group indicates a lack of algorithmic fairness.

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Accountability

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Transparency

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Privacy

Question 6

A team deploys a real-time fraud detection model on a streaming platform. The model must produce predictions within 100 milliseconds per event. Initial latency is 150 ms. Which optimization is most likely to meet the latency requirement?

Accepted Answer

Apply model quantization to reduce precision from FP32 to INT8.. Model quantization reduces the numerical precision of the model's weights and activations from FP32 to INT8, which decreases memory footprint and speeds up inference. This optimization directly addresses the 150 ms latency by enabling faster arithmetic operations on modern hardware, often cutting inference time by 2-4x, which can bring latency below the 100 ms requirement.

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Increase the batch size to process more events simultaneously.

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Add more feature engineering steps to improve model accuracy.

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Migrate from a decision tree ensemble to a deep neural network.

Question 7

Refer to the exhibit. A system administrator reviews the deployment. Which action should be taken to meet the SLA?

Accepted Answer

Scale up the compute resources. The exhibit shows a deployment where inference latency exceeds the SLA requirement. Scaling up compute resources (e.g., adding more CPU cores, GPU memory, or increasing instance size) directly reduces per-request processing time by providing more parallel processing capacity, which is the most straightforward way to meet latency SLAs when the model is already optimized.

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Retrain the model

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Implement caching

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Reduce model input size

Question 8

An e-commerce company deploys a model to recommend products to users. The recommendation system uses collaborative filtering based on user-item interaction history. After deployment, the model shows decreasing click-through rates (CTR) over time. The data engineer notices that the model was trained on data from the past six months and is retrained daily. However, the trend suggests that user preferences are shifting more rapidly than expected. The engineer suspects that the model is suffering from distribution drift. Which approach should the engineer implement to adapt the model more quickly to changing user behavior?

Accepted Answer

Switch to an online learning algorithm that updates the model after each user click. Online learning algorithms update the model incrementally with each new user click, allowing it to rapidly adapt to shifting user preferences. This directly addresses distribution drift caused by fast-changing behaviors. Option A is wrong because increasing retraining to once per week reduces update frequency, making the model slower to adapt. Option C is wrong because adding complexity does not solve distribution drift and risks overfitting. Option D is wrong because training on only the last week may produce a noisy model and does not provide a mechanism for continuous adaptation.

Answer

Increase the retraining period to once per week to reduce computational cost

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Increase the model complexity by adding more features and layers

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Use only the last week of data for training to focus on recent trends

Question 9

An AI application needs to generate structured JSON output from an LLM. The development team wants to ensure the output always conforms to a specific schema. Which prompt engineering technique is MOST suitable?

Accepted Answer

System prompt with JSON schema and a 'respond only with valid JSON' instruction. Providing the JSON schema directly in the system prompt, combined with an explicit instruction to respond only with valid JSON, is the most direct and reliable way to constrain an LLM's output format. This technique leverages the model's instruction-following capability and schema awareness without requiring examples or retraining, ensuring strict adherence to the desired structure.

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Few-shot examples showing correct JSON

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Chain-of-thought prompting

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Fine-tuning the model on JSON datasets

Question 10

An organization uses an AI-based hiring tool. To prevent bias, they want to ensure the model's decisions are explainable. Which approach is most suitable?

Accepted Answer

Use a simpler interpretable model like logistic regression. A simpler interpretable model like logistic regression provides inherent transparency—its coefficients directly show the weight and direction of each input feature on the hiring decision. This makes it easy for auditors and stakeholders to verify that the model is not using protected attributes (e.g., race, gender) in a biased way, without needing post-hoc explanation tools. The key is that the model itself is interpretable by design, not just explained after the fact.

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Use reinforcement learning with fairness constraints

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Use a black-box deep learning model with SHAP explanations

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Use ensemble methods with feature importance

Question 11

A deep learning model for image classification achieves 99% training accuracy but only 85% validation accuracy. The model has millions of parameters. Which technique is most likely to reduce overfitting while maintaining high accuracy?

Accepted Answer

Add dropout layers with a rate of 0.5 after each convolutional block. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents co-adaptation of features and forces the network to learn more robust representations. With 99% training accuracy and 85% validation accuracy, the model is clearly overfitting, and adding dropout layers with a rate of 0.5 after each convolutional block directly addresses this by reducing the model's capacity to memorize the training data, while still allowing high accuracy on the validation set.

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Reduce batch size from 32 to 8

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Decrease the learning rate by a factor of 10

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Increase the number of training epochs to 500

Question 12

A hospital uses an AI system to predict patient deterioration from vital signs. The system currently uses a logistic regression model trained on data from the past year. Recently, the hospital adopted a new patient monitoring device that provides more accurate readings. The model's performance has dropped significantly. The data science team has access to the new device's data for the past month and wants to improve the model with minimal disruption. The team also wants to ensure the model remains interpretable for regulatory compliance. Which approach should they take?

Accepted Answer

Retrain the logistic regression model on a combined dataset of old and new device data. Retraining the logistic regression model on a combined dataset of old and new device data is the best approach because it leverages all available data to adapt the model to the new device's measurement distribution while preserving the model's inherent interpretability. Logistic regression is a linear model that remains fully transparent for regulatory compliance, and combining both datasets helps the model learn the systematic shift in vital sign readings without discarding valuable historical patterns. This minimizes disruption by avoiding a complete overhaul and directly addresses the performance drop caused by the change in input data distribution.

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Continue using the current model and manually adjust predictions based on device differences

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Build an ensemble of logistic regression and a neural network using new data only

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Replace the logistic regression model with a gradient boosting model using only new device data

Question 13

Which practice best ensures AI systems comply with regulations like GDPR?

Accepted Answer

Data minimization and anonymization. Data minimization and anonymization directly align with GDPR's core principles, such as Article 5(1)(c) which mandates that personal data be 'adequate, relevant and limited to what is necessary.' By collecting only essential data and applying techniques like k-anonymity or differential privacy, AI systems reduce the risk of re-identification and ensure compliance with data protection by design and by default (Article 25). This practice is a foundational governance measure, not a reactive or staffing solution.

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Using open-source models only

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Regular vulnerability scans

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Hiring more data scientists

Question 14

Which THREE components are essential in an MLOps pipeline?

Accepted Answer

Data versioning. Data versioning (A) is essential in an MLOps pipeline because it ensures reproducibility and traceability of datasets used for training, validation, and testing. Without versioning, changes to data cannot be tracked, leading to inconsistent model behavior and difficulty in debugging. Tools like DVC or Git LFS enable precise snapshotting of data, which is critical for auditing and rollback in production AI systems.

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Manual code review

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Hardware procurement

Question 15

A team deploying an AI model for real-time fraud detection notices that inference latency is too high. The model is a deep neural network with 50 layers, deployed on a cloud GPU. Which of the following is the BEST approach to reduce latency while maintaining acceptable accuracy?

Accepted Answer

Apply knowledge distillation to create a smaller model.. Knowledge distillation trains a smaller 'student' model to mimic the behavior of a larger 'teacher' model, significantly reducing the number of parameters and layers while preserving most of the original accuracy. This directly addresses the high inference latency caused by the 50-layer DNN by producing a compact model that runs faster on the same GPU hardware.

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Deploy the model on a more powerful GPU.

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Reduce the batch size for inference.

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Replace the DNN with a logistic regression model.