Practice - 5 Tasks

Answer the questions below

1fill in blank

easy

Complete the SQL query to select all records from the retraining_schedule table.

MLOps

SELECT [1] FROM retraining_schedule;

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

Amodel_name

Bschedule_id

Cretrain_date

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting only one column instead of all columns.

Using incorrect column names.

✗ Incorrect

The * symbol selects all columns from the table.

2fill in blank

medium

Complete the SQL query to find retraining events scheduled after January 1, 2024.

MLOps

SELECT * FROM retraining_schedule WHERE retrain_date [1] '2024-01-01';

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

C<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < which selects dates before the given date.

Using = which selects only that exact date.

✗ Incorrect

The > operator selects dates after January 1, 2024.

3fill in blank

hard

Fix the error in the query to count retraining events triggered by drift.

MLOps

SELECT COUNT(*) FROM retraining_events WHERE trigger_type = [1];

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

A'drift'

Bdrift

C"drift"

Ddrift'

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around string values.

Using mismatched or double quotes incorrectly.

✗ Incorrect

String values in SQL must be enclosed in single quotes.

4fill in blank

hard

Fill both blanks to select model names and their last retraining date for models with performance below 0.8.

MLOps

SELECT model_name, [1] FROM model_performance WHERE performance [2] 0.8;

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

Alast_retrain_date

Dperformance_date

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting the wrong date column.

Using '>' instead of '<' for filtering performance.

✗ Incorrect

Select the last retraining date and filter models with performance less than 0.8.

5fill in blank

hard

Fill all three blanks to create a dictionary of model names to their retraining count where retraining was triggered by schedule.

MLOps

SELECT [1], COUNT(*) AS retrain_count FROM retraining_events WHERE trigger_type = [2] GROUP BY [3];

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

Amodel_name

B'schedule'

Dtrigger_type

Attempts:

3 left

💡 Hint

Common Mistakes

Not grouping by model_name.

Not quoting the string 'schedule'.

Using wrong columns in SELECT or GROUP BY.

✗ Incorrect

Select model names, count retraining events triggered by schedule, and group by model name.

Practice

(1/5)

1. What is the main purpose of trigger-based retraining in machine learning operations?

easy

A. Automatically update models when data or performance changes

B. Manually retrain models on a fixed schedule

C. Store training data in a database

D. Visualize model performance metrics

Solution

Step 1: Understand trigger-based retraining concept
Trigger-based retraining means models update automatically when certain conditions happen, like data changes or performance drops.
Step 2: Compare options to concept
Only Automatically update models when data or performance changes describes automatic updates based on triggers, matching the concept.
Final Answer:
Automatically update models when data or performance changes -> Option A
Quick Check:
Trigger-based retraining = automatic updates [OK]

Hint: Triggers mean automatic updates, not manual tasks [OK]

Common Mistakes:

Confusing manual retraining with trigger-based retraining
Thinking triggers only store data
Assuming triggers visualize data

2. Which SQL statement correctly creates a trigger to start retraining after new data is inserted into a table named training_data?

easy

A. CREATE retrain_trigger AFTER INSERT ON training_data CALL start_retraining();

B. INSERT TRIGGER retrain_trigger ON training_data AFTER EXEC start_retraining();"

C. TRIGGER CREATE retrain_trigger ON training_data AFTER INSERT EXEC start_retraining();

D. CREATE TRIGGER retrain_trigger AFTER INSERT ON training_data FOR EACH ROW EXECUTE PROCEDURE start_retraining();

Solution

Step 1: Recall correct SQL trigger syntax
Standard SQL triggers use CREATE TRIGGER, specify timing (AFTER), event (INSERT), table, and procedure to execute.
Step 2: Match syntax to options
CREATE TRIGGER retrain_trigger AFTER INSERT ON training_data FOR EACH ROW EXECUTE PROCEDURE start_retraining(); matches correct syntax: CREATE TRIGGER retrain_trigger AFTER INSERT ON training_data FOR EACH ROW EXECUTE PROCEDURE start_retraining();
Final Answer:
CREATE TRIGGER retrain_trigger AFTER INSERT ON training_data FOR EACH ROW EXECUTE PROCEDURE start_retraining(); -> Option D
Quick Check:
Correct trigger syntax = CREATE TRIGGER retrain_trigger AFTER INSERT ON training_data FOR EACH ROW EXECUTE PROCEDURE start_retraining(); [OK]

Hint: Look for 'CREATE TRIGGER ... EXECUTE PROCEDURE' pattern [OK]

Common Mistakes:

Using CALL instead of EXECUTE PROCEDURE
Wrong order of keywords
Missing FOR EACH ROW clause

3. Given this trigger function in PostgreSQL:

CREATE OR REPLACE FUNCTION check_drift() RETURNS trigger AS $$
BEGIN
  IF NEW.error_rate > 0.1 THEN
    PERFORM start_retraining();
  END IF;
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

What happens when a new row with error_rate = 0.15 is inserted?

medium

A. The retraining procedure is called because error_rate > 0.1

B. Nothing happens because triggers don't run on INSERT

C. An error occurs due to syntax mistake

D. The row is rejected and not inserted

Solution

Step 1: Analyze trigger function logic
The function checks if NEW.error_rate > 0.1; if true, it calls start_retraining().
Step 2: Apply condition to given data
Since error_rate is 0.15, which is greater than 0.1, the retraining procedure is called.
Final Answer:
The retraining procedure is called because error_rate > 0.1 -> Option A
Quick Check:
error_rate 0.15 > 0.1 triggers retraining [OK]

Hint: Check condition in trigger function with inserted data [OK]

Common Mistakes:

Thinking triggers don't run on INSERT
Assuming syntax error without checking code
Believing row insertion fails

4. You wrote this trigger to start retraining on performance drop:

CREATE TRIGGER retrain_on_drop
AFTER UPDATE ON model_metrics
FOR EACH ROW
WHEN (NEW.accuracy < OLD.accuracy)
EXECUTE PROCEDURE start_retraining();

But retraining never starts. What is the likely problem?

medium

A. Triggers cannot run AFTER UPDATE events

B. The WHEN clause is not supported in all SQL dialects

C. start_retraining() must be a procedure, not a function

D. The trigger name is invalid

Solution

Step 1: Understand WHEN clause support
Not all SQL databases support the WHEN clause in triggers; some require condition checks inside the function.
Step 2: Identify why retraining doesn't start
If the database ignores the WHEN clause, the condition is never checked, so retraining never triggers.
Final Answer:
The WHEN clause is not supported in all SQL dialects -> Option B
Quick Check:
WHEN clause support varies by SQL dialect [OK]

Hint: Check if your SQL dialect supports WHEN in triggers [OK]

Common Mistakes:

Assuming triggers can't run AFTER UPDATE
Confusing functions and procedures
Thinking trigger names cause failure

5. You want to design a trigger-based retraining system that retrains a model only if both the data drift exceeds threshold and model accuracy drops below 90%. Which approach is best?

hard

A. Manually retrain the model when you notice performance issues

B. Create two separate triggers: one for drift and one for accuracy, each retraining independently

C. Create a trigger that calls a procedure checking both drift and accuracy before retraining

D. Schedule retraining daily regardless of drift or accuracy

Solution

Step 1: Understand combined condition requirement
The retraining should happen only if both drift and accuracy conditions are met together.
Step 2: Evaluate options for combined logic
Create a trigger that calls a procedure checking both drift and accuracy before retraining uses a single trigger calling a procedure that checks both conditions before retraining, ensuring both must be true.
Step 3: Why other options fail
Create two separate triggers: one for drift and one for accuracy, each retraining independently retrains independently on each condition, not requiring both. Schedule retraining daily regardless of drift or accuracy ignores conditions. Manually retrain the model when you notice performance issues is manual, not trigger-based.
Final Answer:
Create a trigger that calls a procedure checking both drift and accuracy before retraining -> Option C
Quick Check:
Combined condition needs single trigger with logic [OK]

Hint: Use one trigger with combined condition check procedure [OK]

Common Mistakes:

Using separate triggers causing unnecessary retraining
Ignoring condition checks in triggers
Relying on manual retraining only

Trigger-based retraining (schedule, drift, performance) in MLOps - Interactive Code Practice

Start learning this pattern below

Practice

Solution

Step 1: Understand trigger-based retraining concept

Step 2: Compare options to concept

Final Answer:

Quick Check:

Solution

Step 1: Recall correct SQL trigger syntax

Step 2: Match syntax to options

Final Answer:

Quick Check:

Solution

Step 1: Analyze trigger function logic

Step 2: Apply condition to given data

Final Answer:

Quick Check:

Solution

Step 1: Understand WHEN clause support

Step 2: Identify why retraining doesn't start

Final Answer:

Quick Check:

Solution

Step 1: Understand combined condition requirement

Step 2: Evaluate options for combined logic

Step 3: Why other options fail

Final Answer:

Quick Check: