DA0-001 Analyzing and Modeling Data — All Questions With Answers

SELECT department, COUNT(*) as employee_count FROM employees WHERE hire_year > 2020 GROUP BY department HAVING COUNT(*) > 5;

{"model_type": "random_forest", "n_estimators": 100, "max_depth": 5, "criterion": "gini"}

2024-01-15 10:23:45 ERROR: DataTypeMismatchException - Column 'age' contains mixed data types: INT and VARCHAR. Pipeline 'user_profile_etl' failed.

Refer to the exhibit.

Python pandas code and output:
```
import pandas as pd
df = pd.read_csv('employees.csv')
df['salary'].fillna(df['salary'].median(), inplace=True)
print(df['salary'].describe())
```
Output:
```
count    1000.000000
mean     55000.000000
std      15000.000000
min      25000.000000
25%      45000.000000
50%      52000.000000
75%      65000.000000
max     120000.000000
Name: salary, dtype: float64
```

Refer to the exhibit.

JSON policy:
```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::data-lake/*"
    }
  ]
}
```

Refer to the exhibit.

SELECT customer_id, COUNT(order_id) AS order_count
FROM orders
WHERE order_date BETWEEN '2023-01-01' AND '2023-12-31'
GROUP BY customer_id
HAVING COUNT(order_id) > 5;

Refer to the exhibit.

Call:
lm(formula = price ~ sqft_living + bedrooms + bathrooms, data = housing)

Residuals:
    Min      1Q  Median      3Q     Max
-1.2345 -0.3456 -0.0123  0.3456  2.3456

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)   0.123456   0.012345  10.000  <2e-16 ***
sqft_living   0.001234   0.000123  10.000  <2e-16 ***
bedrooms     -0.056789   0.012345  -4.600  4.23e-06 ***
bathrooms     0.234567   0.045678   5.135  3.45e-07 ***
--
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4567 on 496 degrees of freedom
Multiple R-squared:  0.789, Adjusted R-squared:  0.787
F-statistic: 617.8 on 3 and 496 DF,  p-value: < 2.2e-16

Refer to the exhibit.

import pandas as pd
df = pd.read_csv('data.csv')
df['total'] = df['price'] * df['quantity']
df.head()