Model registry in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of a Model Registry

Which of the following best describes the main purpose of a model registry in machine learning projects?

A To store, version, and manage machine learning models for easy deployment and tracking

B To train machine learning models faster using distributed computing

C To collect raw data from various sources for model training

D To visualize model predictions in real-time dashboards

💡 Hint

Think about how teams keep track of different versions of models and their metadata.

A model registry is a centralized place to store and manage machine learning models, including their versions, metadata, and deployment status. It helps teams track and deploy models efficiently.

❓ Model Choice

intermediate

Choosing Model Version in Registry

You have three versions of a model registered: v1, v2, and v3. v3 has the best accuracy but is unstable in production. Which version should you deploy from the registry?

A Deploy v3 because it has the best accuracy

B Deploy v2 if it balances accuracy and stability

C Deploy v1 because it is the first version

D Deploy none and retrain a new model

💡 Hint

Consider both accuracy and stability for production use.

The best model to deploy balances accuracy and stability. Even if v3 has the highest accuracy, instability can cause failures. Choosing v2 if it is stable and reasonably accurate is best.

❓ Metrics

advanced

Tracking Model Metrics in Registry

Which metric is NOT typically tracked in a model registry to evaluate model performance?

A User interface color scheme

B Model accuracy on validation data

C Training time duration

D Number of model parameters

💡 Hint

Think about metrics related to model quality and resource use.

Model registries track metrics like accuracy, training time, and model size to evaluate and compare models. User interface color scheme is unrelated to model performance.

🔧 Debug

advanced

Identifying Issue in Model Registry Usage

A team notices that after registering a new model version, the deployment still uses the old model. What is the most likely cause?

A The model registry deleted the new model version automatically

B The new model version has a syntax error in the training code

C The training data was not updated before retraining

D The deployment pipeline is not configured to pull the latest model version from the registry

💡 Hint

Think about how deployment pipelines interact with model registries.

If deployment still uses the old model, it usually means the pipeline is not set to fetch or update to the latest registered model version.

❓ Predict Output

expert

Output of Model Registry Version Query Code

What is the output of this Python code snippet querying a model registry?

ML Python

model_versions = ['v1', 'v2', 'v3']
registered_versions = {v: 'deployed' if v != 'v2' else 'staging' for v in model_versions}
print(registered_versions['v2'])

A KeyError

B deployed

C staging

D SyntaxError

💡 Hint

Check the dictionary comprehension logic carefully.

The dictionary comprehension sets 'staging' for version 'v2' and 'deployed' for others. So registered_versions['v2'] returns 'staging'.

Practice

1.

What is the main purpose of a model registry in machine learning?

easy

A. To store and organize machine learning models

B. To train machine learning models faster

C. To clean and prepare data for training

D. To visualize model predictions

Solution

1. Step 1: Understand the role of a model registry

   A model registry is designed to keep track of machine learning models, their versions, and metadata.

2. Step 2: Compare options with the definition

   Only To store and organize machine learning models describes storing and organizing models, which matches the main purpose of a model registry.

3. Final Answer:

   To store and organize machine learning models -> Option A

4. Quick Check:

   Model registry = store and organize models [OK]

Hint: Model registry = place to keep models safe and sorted [OK]

Common Mistakes:

• Confusing model registry with data cleaning
• Thinking it speeds up training
• Mixing it with visualization tools

2.

Which of the following is the correct way to add a model to a simple Python dictionary used as a model registry?

model_registry = {}
# Add model here

easy

A. model_registry['model_v1'] = trained_model

B. model_registry.add('model_v1', trained_model)

C. model_registry.append('model_v1', trained_model)

D. model_registry.insert('model_v1', trained_model)

Solution

1. Step 1: Recall how to add key-value pairs to a dictionary

   In Python, adding an item to a dictionary uses the syntax dict[key] = value.

2. Step 2: Check each option for correct dictionary syntax

   The incorrect options use methods like .add(), .append(), and .insert() which are not available for dictionaries. Only model_registry['model_v1'] = trained_model uses the correct assignment syntax.

3. Final Answer:

   model_registry['model_v1'] = trained_model -> Option A

4. Quick Check:

   Dictionary add = dict[key] = value [OK]

Hint: Use dict[key] = value to add items in Python [OK]

Common Mistakes:

• Using list methods like append on dictionaries
• Trying to call add or insert on dict
• Confusing dictionary with list syntax

3.

Given the following code, what will be the output?

model_registry = {}
model_registry['v1'] = {'accuracy': 0.8}
model_registry['v2'] = {'accuracy': 0.85}
print(model_registry['v2']['accuracy'])

medium

A. 0.8

B. 0.85

C. {'accuracy': 0.85}

D. KeyError

Solution

1. Step 1: Understand the dictionary structure

   model_registry has keys 'v1' and 'v2', each storing a dictionary with an 'accuracy' key.

2. Step 2: Access the nested value

   model_registry['v2']['accuracy'] accesses the accuracy value for version 'v2', which is 0.85.

3. Final Answer:

   0.85 -> Option B

4. Quick Check:

   Access nested dict value = 0.85 [OK]

Hint: Access nested dict with dict[key1][key2] [OK]

Common Mistakes:

• Confusing keys and values
• Expecting full dict instead of value
• Mistyping keys causing KeyError

4.

Identify the error in this code snippet that tries to update a model's accuracy in a registry:

model_registry = {'v1': {'accuracy': 0.8}}
model_registry.update('v1', {'accuracy': 0.85})

medium

A. Syntax error due to missing colon

B. update cannot update nested dictionaries

C. The key 'v1' does not exist in the dictionary

D. update method expects a dictionary, not two arguments

Solution

1. Step 1: Check the usage of dictionary update method

   The update() method takes a single dictionary or iterable of key-value pairs, not separate arguments.

2. Step 2: Identify the correct way to update nested dictionary

   To update nested dict, access it first: model_registry['v1'].update({'accuracy': 0.85}).

3. Final Answer:

   update method expects a dictionary, not two arguments -> Option D

4. Quick Check:

   dict.update() needs one dict argument [OK]

Hint: dict.update() takes one dict, not multiple args [OK]

Common Mistakes:

• Passing multiple arguments to update()
• Trying to update nested dict without accessing it
• Assuming update() works like assignment

5.

You want to keep track of multiple versions of a model with their accuracy and deployment status. Which data structure best suits a simple model registry in Python?

hard

A. A set of model names without details

B. A list of tuples with version and accuracy only

C. A dictionary with version keys and nested dictionaries for metrics and status

D. A single string describing the latest model

Solution

1. Step 1: Identify the need for storing multiple details per version

   You need to store version keys, accuracy, and deployment status, which requires nested information.

2. Step 2: Evaluate data structures for this need

   A dictionary with version keys and nested dictionaries for details fits best. Lists or sets lack easy key-based access or nested info.

3. Final Answer:

   A dictionary with version keys and nested dictionaries for metrics and status -> Option C

4. Quick Check:

   Nested dicts store complex model info best [OK]

Hint: Use nested dicts for versions and details [OK]

Common Mistakes:

• Using lists or sets that lack keys
• Storing only one detail per model
• Using strings that can't hold structured data