ML model training in Snowflake - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a machine learning model in Snowflake.

Snowflake

CREATE MODEL my_model FROM my_table PREDICTING target USING [1]; 

Drag options to blanks, or click blank then click option'

ASELECT

BINSERT

CLINEAR_REGRESSION

DUPDATE

Attempts:

3 left

💡 Hint

Common Mistakes

Using SQL commands like SELECT or INSERT instead of a model type.

Omitting the model type after PREDICTING.

The CREATE MODEL statement requires specifying the model type, such as LINEAR_REGRESSION, to train a model in Snowflake.

2fill in blank

medium

Complete the code to specify the input features for the model training.

Snowflake

CREATE MODEL my_model FROM my_table PREDICTING target USING LINEAR_REGRESSION INPUT ([1]);

Drag options to blanks, or click blank then click option'

AWHERE feature1 > 0

Btarget, feature1

CSELECT *

Dfeature1, feature2, feature3

Attempts:

3 left

💡 Hint

Common Mistakes

Including the target column as a feature.

Using SQL clauses like SELECT or WHERE inside INPUT.

The INPUT clause lists the columns used as input features for training the model. Only feature columns should be included, not the target.

3fill in blank

hard

Fix the error in the model training command by completing the missing clause.

Snowflake

CREATE MODEL my_model [1] my_table PREDICTING target USING LINEAR_REGRESSION INPUT (feature1, feature2);

Drag options to blanks, or click blank then click option'

AFROM

BWHERE

CSELECT

DJOIN

Attempts:

3 left

💡 Hint

Common Mistakes

Using WHERE or JOIN instead of FROM after INPUT.

Omitting the FROM clause entirely.

The FROM clause specifies the source table for training data in the model creation statement.

4fill in blank

hard

Fill both blanks to evaluate the trained model using SQL.

Snowflake

SELECT predicted_label, actual_label FROM TABLE([1](MODEL => 'my_model', DATA => [2]));

Drag options to blanks, or click blank then click option'

AML.PREDICT

Bmy_table

Ctraining_data

DML.TRAIN

Attempts:

3 left

💡 Hint

Common Mistakes

Using ML.TRAIN instead of ML.PREDICT for predictions.

Using a wrong table name or a keyword instead of a table.

The ML.PREDICT function is used to get predictions from the model, and the data table (e.g., my_table) provides the input data for prediction.

5fill in blank

hard

Fill all three blanks to create a model, train it, and evaluate accuracy.

Snowflake

CREATE MODEL my_model FROM training_data PREDICTING target USING [1] INPUT (feature1, feature2);

SELECT accuracy FROM TABLE([2](MODEL => 'my_model', DATA => test_data)) WHERE metric = '[3]';

Drag options to blanks, or click blank then click option'

ALOGISTIC_REGRESSION

BML.EVALUATE

Caccuracy

DML.PREDICT

Attempts:

3 left

💡 Hint

Common Mistakes

Using ML.PREDICT instead of ML.EVALUATE to get accuracy.

Using regression model type for classification tasks.

Misspelling the metric name.

First, specify the model type LOGISTIC_REGRESSION to create the model. Then use ML.EVALUATE to get evaluation metrics. Finally, filter by the metric name accuracy to see the model's accuracy.

Practice

1. What is the main purpose of the CREATE MODEL statement in Snowflake's ML feature?

easy

A. To delete an existing machine learning model

B. To export model results to an external file

C. To make predictions using a trained model

D. To define and train a machine learning model using SQL

Solution

1. Step 1: Understand the role of CREATE MODEL

   This statement is used to define the model type, label column, and features, which means it sets up and trains the model.

2. Step 2: Differentiate from other commands

   PREDICT is used for predictions, not CREATE MODEL. Deleting or exporting models uses other commands.

3. Final Answer:

   To define and train a machine learning model using SQL -> Option D

4. Quick Check:

   CREATE MODEL = train model [OK]

Hint: Remember: CREATE MODEL means build and train [OK]

Common Mistakes:

• Confusing CREATE MODEL with PREDICT
• Thinking CREATE MODEL deletes models
• Assuming CREATE MODEL exports data

2. Which of the following is the correct syntax to create a logistic regression model named my_model in Snowflake?

easy

A. CREATE MODEL my_model AS logistic_regression WITH LABEL target AND FEATURES age, income;

B. CREATE MODEL my_model TYPE = 'logistic_regression' LABEL = target FEATURES = (age, income);

C. CREATE MODEL my_model USING logistic_regression LABEL target FEATURES (age, income);

D. CREATE MODEL my_model TYPE logistic_regression LABEL target FEATURES (age, income);

Solution

1. Step 1: Recall Snowflake's CREATE MODEL syntax

   The correct syntax uses TYPE = 'model_type', LABEL = label_column, and FEATURES = (feature_list) with equal signs and parentheses.

2. Step 2: Compare options

   CREATE MODEL my_model TYPE = 'logistic_regression' LABEL = target FEATURES = (age, income); matches the correct syntax exactly. Others have wrong keywords or missing equal signs or parentheses.

3. Final Answer:

   CREATE MODEL my_model TYPE = 'logistic_regression' LABEL = target FEATURES = (age, income); -> Option B

4. Quick Check:

   Correct syntax uses TYPE =, LABEL =, FEATURES = ( ) [OK]

Hint: Look for equal signs and parentheses in syntax [OK]

Common Mistakes:

• Omitting equal signs (=) in parameters
• Missing parentheses around features
• Using wrong keywords like AS or WITH

3. Given the following Snowflake SQL code:

CREATE MODEL churn_model TYPE = 'random_forest' LABEL = churn FEATURES = (age, balance, transactions);
SELECT * FROM PREDICT(churn_model, (SELECT age, balance, transactions FROM customers_test));

What will be the output of the PREDICT statement?

medium

A. The original customers_test table without predictions

B. An error because PREDICT cannot use SELECT statements

C. A table with predicted churn values for each row in customers_test

D. A summary report of model accuracy

Solution

1. Step 1: Understand the PREDICT statement

   PREDICT applies the trained model to new data and returns predicted values for each input row.

2. Step 2: Analyze the code

   The SELECT statement provides features from customers_test. PREDICT will output predictions for each row based on these features.

3. Final Answer:

   A table with predicted churn values for each row in customers_test -> Option C

4. Quick Check:

   PREDICT outputs predictions per input row [OK]

Hint: PREDICT returns predictions for each input row [OK]

Common Mistakes:

• Thinking PREDICT returns original data only
• Assuming PREDICT gives accuracy metrics
• Believing PREDICT cannot use SELECT

4. You run this Snowflake command:

CREATE MODEL sales_model TYPE = 'linear_regression' LABEL = sales_amount FEATURES = (store_size, location);

But get an error saying "Invalid feature column: location". What is the most likely cause?

medium

A. The column 'location' is not numeric or not suitable as a feature

B. The LABEL column 'sales_amount' does not exist

C. The model type 'linear_regression' is not supported

D. The FEATURES list must be empty for linear regression

Solution

1. Step 1: Understand feature requirements

   Features must be numeric or convertible to numeric for linear regression models.

2. Step 2: Analyze the error message

   "Invalid feature column" suggests 'location' is likely categorical or text, which linear regression cannot use directly.

3. Final Answer:

   The column 'location' is not numeric or not suitable as a feature -> Option A

4. Quick Check:

   Features must be numeric for linear regression [OK]

Hint: Check feature columns are numeric for regression models [OK]

Common Mistakes:

• Assuming LABEL column causes this error
• Thinking model type is unsupported
• Believing features list can be empty

5. You want to train a classification model in Snowflake to predict customer churn. You have a dataset with columns: customer_id, age, monthly_spend, churned (1 if churned, 0 otherwise), and signup_date. Which CREATE MODEL statement correctly trains a model ignoring customer_id and signup_date?

hard

A. CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = churned FEATURES = (age, monthly_spend);

B. CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = customer_id FEATURES = (age, monthly_spend, churned);

C. CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = churned FEATURES = (customer_id, age, monthly_spend, signup_date);

D. CREATE MODEL churn_model TYPE = 'linear_regression' LABEL = churned FEATURES = (age, monthly_spend);

Solution

1. Step 1: Identify correct label and features

   The label is churned because it indicates churn. Features should exclude customer_id and signup_date as they are identifiers or dates not useful for prediction.

2. Step 2: Choose appropriate model type

   Since churn is a binary outcome, logistic regression is suitable, not linear regression.

3. Final Answer:

   CREATE MODEL churn_model TYPE = 'logistic_regression' LABEL = churned FEATURES = (age, monthly_spend); -> Option A

4. Quick Check:

   Label = churned, features exclude IDs/dates, logistic regression for classification [OK]

Hint: Use label as target, exclude IDs/dates from features [OK]

Common Mistakes:

• Using ID or date columns as features
• Choosing wrong label column
• Using linear regression for classification