Refer to the Exhibit Practice Questions

Trigger automatic retraining using the latest data

Why wrong: Auto-retraining without analysis could introduce bad patterns.

Roll back to the previous model version immediately

Why wrong: Rollback might be unnecessary if drift is harmless.

Ignore the drift since performance metrics are stable

Why wrong: Drift often precedes performance drop; ignoring is risky.

The policy allows access from a wide private IP range

Why wrong: While the policy allows a wide private IP range, it is still restricted to corporate networks, making this less significant than unlimited inference requests.

The policy does not require multi-factor authentication

Why wrong: Multi-factor authentication is explicitly required in the policy, so this is not a weakness.

The policy grants 'audit_log' access to data scientists

Why wrong: Granting audit_log access to data scientists is a minor access control issue but not as critical as model extraction risk.

The model is overfitting

Why wrong: Overfitting shows decreasing training loss with increasing validation loss; here training loss is increasing.

The model is underfitting

Why wrong: Underfitting would show high loss but not necessarily increasing loss.

The batch size is too large

Why wrong: Large batch size can affect convergence but typically does not cause such rapid divergence.

ChurnPredict v1

Why wrong: ChurnPredict v1 uses an interpretable model and is compliant.

FraudDetect v4

Why wrong: FraudDetect v4 uses an interpretable model and is compliant.

LoanApproval v3

Why wrong: LoanApproval v3 uses an interpretable model and is compliant.

Decrease the classification threshold

Why wrong: Decreasing threshold increases recall but may also increase false positives; however, the question asks for most likely effective, and data collection is a fundamental approach.

Increase the classification threshold

Why wrong: Increasing threshold reduces recall.

Add more features

Why wrong: Adding features may help but not as directly as addressing class imbalance.

0.72

Why wrong: 0.72 is approximate accuracy (150/200=0.75, not 0.72).

0.7

Why wrong: 0.7 is specificity (70/100=0.7).

0.73

Why wrong: 0.73 is approximate precision (80/(80+30)=0.727).

Change the activation to tanh

Why wrong: Tanh is similar to relu but may not resolve high learning rate issues.

Add dropout after each layer

Why wrong: Dropout regularizes but does not fix optimization instability caused by high learning rate.

Increase the number of units in the first dense layer

Why wrong: Adding units increases model capacity but does not directly address fluctuating loss.

Remove rows with null income

Why wrong: Removing 12 rows may lose data, especially if missingness is not random.

Replace nulls with 0

Why wrong: Zero income is unrealistic and would introduce bias.

Replace nulls with the mean income of the entire dataset

Why wrong: Mean may be affected by regional differences and outliers.

Learning rate is too low; increase it

Why wrong: Loss is decreasing steadily, so learning rate is not too low.

Underfitting; increase model complexity

Why wrong: Training loss is low, so not underfitting.

Data imbalance; use class weights

Why wrong: There is no indication of class imbalance from the loss values.

Add more training data.

Why wrong: Adding data increases memory requirements and would worsen the error.

Increase the learning rate.

Why wrong: Learning rate does not affect memory usage.

Switch to a CPU.

Why wrong: CPU may still run out of memory if the problem is not addressed, and training would be slower.

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

Why wrong: That alone wouldn't cause denial if not explicitly denied.

The policy JSON has a syntax error

Why wrong: The JSON is valid.

The endpoint path is incorrect

Why wrong: Exhibit shows correct path.

Vanishing gradients

Why wrong: Vanishing gradients would cause slow decrease in loss.

Underfitting

Why wrong: Underfitting would have high training loss as well.

Data leakage

Why wrong: Data leakage would cause abnormally high accuracy.

Model accuracy below threshold

Why wrong: Overall accuracy is 0.98, which is high and above typical thresholds.

High latency

Why wrong: The average latency of 45 ms is acceptable for real-time applications and not a critical concern.

Low throughput

Why wrong: Throughput of 1200 requests per second is high and not an issue.

Underfitting

Why wrong: Incorrect; underfitting would have low accuracy on both training and validation.

Data augmentation failure

Why wrong: Incorrect; data augmentation would affect training data diversity, not directly indicated.

Model compression

Why wrong: Incorrect; model compression reduces model size, not shown here.

Review the fairness check settings to ensure protected attributes are still relevant.

Why wrong: Fairness is separate; drift detection triggered due to feature drift, not fairness.

Immediately retrain the model on recent data to adapt to the drift.

Why wrong: Retraining without analyzing the nature of drift may lead to suboptimal model.

Reduce the classification threshold to 0.5 to increase sensitivity.

Why wrong: Threshold change does not address feature drift.