ML lifecycle stages in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the correct order of ML lifecycle stages

Which of the following lists the ML lifecycle stages in the correct order?

A Model Training, Data Collection, Model Deployment, Model Monitoring

B Data Collection, Model Deployment, Model Training, Model Monitoring

C Model Deployment, Data Collection, Model Training, Model Monitoring

D Data Collection, Model Training, Model Deployment, Model Monitoring

💡 Hint

Think about what you need before training a model and what happens after deployment.

The ML lifecycle starts with collecting data, then training the model, deploying it, and finally monitoring its performance.

💻 Command Output

intermediate

Output of model evaluation metric calculation

Given the following Python code snippet that calculates accuracy, what is the output?

MLOps

from sklearn.metrics import accuracy_score
true_labels = [1, 0, 1, 1, 0]
predicted_labels = [1, 0, 0, 1, 0]
accuracy = accuracy_score(true_labels, predicted_labels)
print(f"Accuracy: {accuracy:.2f}")

A Accuracy: 1.00

B Accuracy: 0.60

C Accuracy: 0.80

D Accuracy: 0.40

💡 Hint

Count how many predictions match the true labels and divide by total.

4 out of 5 predictions are correct, so accuracy is 0.8.

🔀 Workflow

advanced

Correct sequence of steps for model deployment

Which option shows the correct sequence of steps to deploy a machine learning model in production?

A Deploy to cloud, Containerize model, Monitor model, Set up CI/CD pipeline

B Containerize model, Set up CI/CD pipeline, Deploy to cloud, Monitor model

C Monitor model, Deploy to cloud, Containerize model, Set up CI/CD pipeline

D Set up CI/CD pipeline, Deploy to cloud, Containerize model, Monitor model

💡 Hint

Think about preparing the model first, then automating deployment, then monitoring.

First, package the model (containerize), then automate deployment (CI/CD), deploy it, and finally monitor.

❓ Troubleshoot

advanced

Troubleshoot model monitoring alert

A deployed ML model suddenly shows a drop in accuracy. Which is the most likely cause?

A Data drift causing input data to differ from training data

B Model training completed successfully

C Model deployment pipeline is running smoothly

D Monitoring system is turned off

💡 Hint

Think about what can change in data after deployment.

Data drift means new data is different from training data, causing accuracy to drop.

✅ Best Practice

expert

Best practice for continuous integration in ML lifecycle

Which practice best supports continuous integration (CI) in the ML lifecycle?

A Automate testing of data preprocessing, model training, and deployment scripts

B Manually run model training after every code change

C Deploy models without version control to speed up delivery

D Ignore data validation to focus on faster model training

💡 Hint

CI means automating checks to catch problems early.

Automating tests for all ML pipeline parts ensures reliable integration and faster feedback.

Practice

1. Which stage in the ML lifecycle involves collecting and preparing data for training?

easy

A. Model Training

B. Data Preparation

C. Model Monitoring

D. Model Deployment

Solution

1. Step 1: Understand the role of data in ML lifecycle

   Data must be collected and cleaned before training a model.

2. Step 2: Identify the stage focused on data tasks

   Data Preparation is the stage where data is gathered and made ready for training.

3. Final Answer:

   Data Preparation -> Option B

4. Quick Check:

   Data Preparation = Collecting and cleaning data [OK]

Hint: Data tasks happen before training starts [OK]

Common Mistakes:

• Confusing deployment with data tasks
• Thinking monitoring includes data cleaning
• Mixing training with data preparation

2. Which of the following is the correct order of stages in a typical ML lifecycle?

easy

A. Data Preparation -> Model Training -> Model Deployment -> Model Monitoring

B. Model Deployment -> Model Training -> Data Preparation -> Model Monitoring

C. Model Training -> Data Preparation -> Model Deployment -> Model Monitoring

D. Model Monitoring -> Model Deployment -> Model Training -> Data Preparation

Solution

1. Step 1: Recall the logical flow of ML lifecycle stages

   First, data is prepared, then the model is trained, followed by deployment and monitoring.

2. Step 2: Match the correct sequence from options

   Data Preparation -> Model Training -> Model Deployment -> Model Monitoring correctly lists the stages in order: Data Preparation -> Model Training -> Model Deployment -> Model Monitoring.

3. Final Answer:

   Data Preparation -> Model Training -> Model Deployment -> Model Monitoring -> Option A

4. Quick Check:

   Correct stage order = Data Preparation -> Model Training -> Model Deployment -> Model Monitoring [OK]

Hint: Remember: Prepare data before training [OK]

Common Mistakes:

• Mixing deployment before training
• Starting with monitoring instead of data
• Incorrect stage order

3. Consider this simplified ML lifecycle code snippet:

stages = ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']
for i, stage in enumerate(stages):
    print(f"Stage {i+1}: {stage}")

What will be the output of this code?

medium

A. Stage 1: Model Training
Stage 2: Data Preparation
Stage 3: Model Deployment
Stage 4: Model Monitoring

B. Stage 0: Data Preparation
Stage 1: Model Training
Stage 2: Model Deployment
Stage 3: Model Monitoring

C. Stage 1: Data Preparation
Stage 2: Model Training
Stage 3: Model Deployment
Stage 4: Model Monitoring

D. Stage 1: Data Preparation
Stage 2: Model Deployment
Stage 3: Model Training
Stage 4: Model Monitoring

Solution

1. Step 1: Understand enumerate behavior in the loop

   enumerate(stages) gives index and value starting at 0, but print uses i+1 for stage number.

2. Step 2: Check the order of stages printed

   The loop prints stages in list order with stage numbers 1 to 4 matching the list order.

3. Final Answer:

   Stage 1: Data Preparation Stage 2: Model Training Stage 3: Model Deployment Stage 4: Model Monitoring -> Option C

4. Quick Check:

   Index + 1 matches stage number [OK]

Hint: Remember enumerate starts at 0, add 1 for display [OK]

Common Mistakes:

• Confusing index starting at 0
• Mixing stage order in output
• Printing wrong stage names

4. You have this ML lifecycle stage list:

stages = ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']
stages.remove('Model Training')
print(stages)

What is the output after running this code?

medium

A. ['Data Preparation', 'Model Deployment', 'Model Monitoring']

B. ['Model Training', 'Model Deployment', 'Model Monitoring']

C. ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']

D. Error: 'remove' method not found

Solution

1. Step 1: Understand what stages.remove('Model Training') does

   This removes the first occurrence of 'Model Training' from the list.

2. Step 2: Check the list after removal

   The list now excludes 'Model Training', leaving the other three stages.

3. Final Answer:

   ['Data Preparation', 'Model Deployment', 'Model Monitoring'] -> Option A

4. Quick Check:

   Remove deletes specified item from list [OK]

Hint: remove() deletes the exact item from list [OK]

Common Mistakes:

• Expecting an error from remove()
• Thinking remove deletes by index
• Not updating the list after removal

5. A team wants to automate retraining their ML model when data changes. Which two ML lifecycle stages must be combined in a pipeline to achieve this?

hard

A. Data Preparation and Model Monitoring

B. Model Deployment and Model Monitoring

C. Model Training and Model Deployment

D. Data Preparation and Model Training

Solution

1. Step 1: Identify stages involved in retraining after data changes

   Retraining requires fresh data preparation and then training the model again.

2. Step 2: Select stages that automate retraining

   Data Preparation and Model Training together form the pipeline for retraining.

3. Final Answer:

   Data Preparation and Model Training -> Option D

4. Quick Check:

   Retrain = Prepare data + Train model [OK]

Hint: Retraining needs fresh data prep plus training [OK]

Common Mistakes:

• Confusing deployment with retraining
• Thinking monitoring triggers retraining alone
• Ignoring data preparation before training