AI0-001 Practice Test 2 — 10 Questions

Question 1

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. Option A is correct because 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 2

During an AI model deployment, the operations team notices that inference requests are taking longer than expected. Which component is most likely causing the bottleneck?

Accepted Answer

The machine learning model's size and architecture. The machine learning model's size and architecture directly determine the computational complexity of inference. Larger models with more parameters or deeper architectures require more matrix multiplications and memory bandwidth, which increases latency per request. This is the most common bottleneck in AI deployment because the model itself is the core computation unit, and its inference time scales with its complexity.

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Input data preprocessing pipeline

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API gateway rate limiting

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Database connection pool size

Question 3

During model monitoring, a loan approval model shows disparate impact against a protected group. The model's overall accuracy is high, but the false positive rate for the protected group is 0.12 compared to 0.02 for other groups. Which action should the operations team take first?

Accepted Answer

Retrain the model with reweighted training data to minimize disparity. Option C is correct because retraining the model with reweighted training data directly addresses the root cause of disparate impact—biased historical data—by assigning higher weights to underrepresented groups during training. This technique, often implemented via cost-sensitive learning or sample reweighting, adjusts the model's internal decision boundaries to reduce false positive rate disparities without sacrificing overall accuracy. The operations team should first attempt to mitigate bias at the data level before considering threshold adjustments or model replacement, as reweighting preserves the model's learned patterns while promoting fairness.

Answer

Document the disparity and proceed with deployment because accuracy is high

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Replace the model with a simpler model that is less discriminatory

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Adjust the decision threshold for the protected group to equalize false positive rates

Question 4

A healthcare company must deploy a diagnostic AI model that uses protected health information (PHI). To comply with HIPAA, the operations team needs to ensure data privacy during model inference. Which practice should be implemented?

Accepted Answer

Encrypt all PHI at rest and in transit within the inference pipeline. Option B is correct because HIPAA mandates encryption of protected health information (PHI) both at rest and in transit to safeguard data confidentiality during model inference. Encrypting the entire inference pipeline ensures that even if data is intercepted or accessed without authorization, it remains unreadable. This practice directly addresses the compliance requirement for data privacy without relying on network location or partial obfuscation.

Answer

Run the model on-premises to avoid cloud data transmission

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Mask sensitive fields in the input data before inference

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Apply differential privacy during model training only

Question 5

A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?

Accepted Answer

Apply cost-sensitive learning. Cost-sensitive learning directly modifies the model's loss function to penalize misclassifications of the minority class more heavily than those of the majority class. This approach addresses the root cause of the imbalance—the model's bias toward the majority class—without altering the dataset distribution, making it the most immediate and effective first step.

Answer

Reduce the majority class size

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Use SMOTE to generate synthetic samples

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Collect more data for the minority class

Question 6

An MLOps team automates model deployment with a CI/CD pipeline. A performance regression is detected after deploying a new model version. The team needs to automatically roll back to the previous version. Which approach best enables safe automated rollback?

Accepted Answer

Use a blue/green deployment with automated health checks and traffic switching. Blue/green deployment with automated health checks and traffic switching is the best approach because it allows the team to instantly route all traffic back to the previous (green) environment if the new (blue) version fails health checks. This ensures zero-downtime rollback without manual intervention, directly addressing the need for safe automated rollback in a CI/CD pipeline.

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Maintain a manual rollback script that the operations team can run

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Deploy new models as canary releases and monitor for 24 hours

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Automatically keep the previous model version in storage for later use

Question 7

Refer to the exhibit. A team created an access policy for a fraud detection model endpoint. An intern reports being unable to access the model for testing. Reviewing the policy, what is the most likely cause?

Accepted Answer

The intern's role is explicitly denied in the policy. Option D is correct because the exhibit shows an explicit `Deny` effect for the intern's role in the policy. In AWS IAM (or similar cloud provider) access policies, an explicit deny overrides any allow, so even if the intern's role is listed in allowed roles, the explicit deny will block access. This is a fundamental principle of IAM policy evaluation logic.

Answer

The intern's role is not included in the allowed roles

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The policy JSON has a syntax error

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The endpoint path is incorrect

Question 8

A dataset for a binary classification problem has 95% of samples in class "0" and 5% in class "1". The data scientist trains a logistic regression model and achieves 95% accuracy. Which metric should the scientist primarily use to evaluate model performance?

Accepted Answer

Precision, recall, and F1-score.. In a highly imbalanced dataset (95% class 0, 5% class 1), accuracy is misleading because a model can achieve 95% accuracy by simply predicting the majority class for all samples. Precision, recall, and F1-score provide a more nuanced view of performance on the minority class, which is typically the class of interest in binary classification problems. The F1-score, in particular, balances precision and recall, making it the primary metric for evaluating model effectiveness on imbalanced data.

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R-squared.

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Accuracy.

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Mean squared error.

Question 9

A data scientist is evaluating a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). Which TWO metrics are most appropriate for assessing model performance? (Choose two.)

Accepted Answer

Precision. Precision is appropriate because it measures the proportion of predicted fraud cases that are actually fraudulent, which is critical when false positives (flagging legitimate transactions as fraud) are costly. In a highly imbalanced dataset like this (99% non-fraud), precision directly evaluates the model's ability to avoid overwhelming fraud analysts with false alarms.

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F1 score

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Area under the ROC curve (AUC-ROC)

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Accuracy

Question 10

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