AI0-001 AI Concepts and Foundations — All Questions With Answers

Refer to the exhibit.

```
Epoch 1/10
 - loss: 1.2345 - accuracy: 0.6543 - val_loss: 1.9876 - val_accuracy: 0.4321
Epoch 2/10
 - loss: 1.0123 - accuracy: 0.7123 - val_loss: 2.3456 - val_accuracy: 0.3987
Epoch 3/10
 - loss: 0.8765 - accuracy: 0.7654 - val_loss: 2.8765 - val_accuracy: 0.3654
```

Refer to the exhibit.

```
{
  "dataset": {
    "name": "customer_churn",
    "features": ["age", "tenure", "monthly_charges", "total_charges"],
    "target": "churn",
    "splits": {
      "train": 0.7,
      "test": 0.15,
      "validation": 0.15
    }
  },
  "model": {
    "type": "RandomForestClassifier",
    "params": {
      "n_estimators": 200,
      "max_depth": 10,
      "random_state": 42
    }
  }
}
```

Refer to the exhibit.
```
{
  "PolicyName": "AIInferencePolicy",
  "Version": "2024-10-01",
  "Rules": [
    {
      "Action": "Allow",
      "Model": "llama-3-70b",
      "MaxTokens": 2048,
      "Temperature": 0.7,
      "RateLimit": 100,
      "TimeWindow": "1 hour"
    },
    {
      "Action": "Deny",
      "Model": "*",
      "Reason": "Unapproved model"
    }
  ]
}
```

Refer to the exhibit.
Epoch 1/50 - loss: 2.4503 - val_loss: 2.4512
Epoch 10/50 - loss: 1.2345 - val_loss: 1.3456
Epoch 20/50 - loss: 0.9876 - val_loss: 1.1234
Epoch 30/50 - loss: 0.6543 - val_loss: 0.9876
Epoch 40/50 - loss: 0.4321 - val_loss: 0.8765
Epoch 50/50 - loss: 0.3210 - val_loss: 0.8321

Refer to the exhibit.
{
  "model": {
    "layers": [
      {"type": "Dense", "units": 256, "activation": "relu"},
      {"type": "Dropout", "rate": 0.5},
      {"type": "Dense", "units": 128, "activation": "relu"},
      {"type": "Dropout", "rate": 0.5},
      {"type": "Dense", "units": 10, "activation": "softmax"}
    ],
    "optimizer": {"type": "Adam", "learning_rate": 0.001},
    "loss": "categorical_crossentropy"
  }
}

Refer to the exhibit.

def train_model(X_train, y_train):
    model = RandomForestClassifier(n_estimators=100, max_depth=5)
    model.fit(X_train, y_train)
    return model

# Training
model = train_model(X_train, y_train)

# Evaluation on test set
accuracy = model.score(X_test, y_test)
print('Accuracy:', accuracy)

Refer to the exhibit.

Model Training Log (Epoch 1-5):
Epoch 1/5 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000
Epoch 2/5 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000
Epoch 3/5 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000
Epoch 4/5 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000
Epoch 5/5 - loss: 2.3026 - accuracy: 0.1000 - val_loss: 2.3026 - val_accuracy: 0.1000

Note: The dataset has 10 classes.

Refer to the exhibit.

JSON Policy for Model Deployment:
{
  "model": "sentiment_analysis_v2",
  "threshold": 0.7,
  "fairness_check": {
    "protected_attributes": ["gender", "age_group"],
    "metric": "demographic_parity",
    "tolerance": 0.05
  },
  "explainability": {
    "method": "LIME",
    "num_features": 5
  },
  "monitoring": {
    "drift_detection": {
      "feature_drift": true,
      "prediction_drift": true,
      "alert_threshold": 0.2
    }
  }
}

Epoch 10/10 - loss: 0.01 - accuracy: 0.99 - val_loss: 0.45 - val_accuracy: 0.85

{
  "fairness_metric": "demographic_parity",
  "threshold": 0.1,
  "protected_attributes": ["race", "gender"]
}

Model inference time: 150ms p95, 200ms p99. SLA requirement: 100ms p95.

Training log:
Epoch 1/10 - loss: 0.6932 - accuracy: 0.5023 - val_loss: 0.6941 - val_acc: 0.5001
Epoch 2/10 - loss: 0.6810 - accuracy: 0.5432 - val_loss: 0.7123 - val_acc: 0.4987
Epoch 3/10 - loss: 0.6645 - accuracy: 0.5876 - val_loss: 0.7356 - val_acc: 0.4953
...
Epoch 10/10 - loss: 0.6234 - accuracy: 0.6521 - val_loss: 0.8123 - val_acc: 0.4889