PMLE Matching — 10 Questions

Question 1

Match each model evaluation metric to its use case.

Accepted Answer

Accuracy: Correct when classes are balanced and errors have equal cost. Accuracy is correctly matched with balanced classes and equal error cost; Precision with minimizing false positives; Recall with minimizing false negatives; F1 Score with balancing precision and recall for imbalanced classes. Option E incorrectly pairs Accuracy with 'when false positives are costly' which is actually the domain of Precision. The main trap is confusing accuracy with precision when dealing with asymmetric costs.

Answer

Accuracy: When false positives are costly

Question 2

Match each MLOps practice to its description.

Accepted Answer

CI/CD: Automates the processes of building, testing, and deploying ML models.. This matching question requires associating each MLOps practice (CI/CD, Model Monitoring, Data Versioning) with its correct description. The correct matches are: CI/CD automates building, testing, and deploying ML models (option A); Model Monitoring continuously observes model performance and detects data drift (option C); Data Versioning manages and tracks changes to datasets over time (option E). Common mistakes include confusing CI/CD with monitoring or versioning, or assigning model monitoring to data versioning tasks. The wrong options (B, D, F) are incorrect because they swap responsibilities: B wrongly attributes monitoring to CI/CD, D assigns versioning to monitoring, and F assigns CI/CD to versioning.

Answer

CI/CD: Continuously observes model performance and detects data drift.

Answer

Model Monitoring: Manages and tracks changes to datasets over time.

Answer

Data Versioning: Automates the processes of building, testing, and deploying ML models.

Question 3

Match each ML pipeline component to its description.

Accepted Answer

Data ingestion: Collecting and importing raw data from various sources.. The correct matches are: Data ingestion → Collecting and importing raw data from various sources. Data validation → Checking data quality, integrity, and schema compliance. Feature engineering → Transforming raw data into features that improve model performance. The remaining options are incorrect: Model training is not about assessing model performance (that's evaluation); Model evaluation is not about deploying a model (that's deployment); Model deployment is not about transforming raw data into features (that's feature engineering).

Answer

Model training: The process of assessing model performance using metrics.

Answer

Model evaluation: The process of deploying a model into production.

Answer

Model deployment: The process of transforming raw data into features.

Question 4

Match each Google Cloud AI/ML service to its primary purpose.

Accepted Answer

Vertex AI: End-to-end ML platform for building, training, and deploying models.. In this matching question, the correct pairings are: Option A (Vertex AI: End-to-end ML platform) is correct because Vertex AI unifies the ML workflow. Option C (Cloud AutoML: Train custom ML models with minimal coding) is correct because AutoML provides no-code training. Option E (Vision API: Analyze images for objects, faces, text) is correct because Vision API specializes in image analysis. Option B is wrong because Vertex AI is not primarily for graphical no-code training—that is AutoML's strength. Option D is wrong because Cloud AutoML is not an end-to-end platform—that's Vertex AI. Option F is wrong because Vision API does not analyze text; that is the Natural Language API's purpose.

Answer

Vertex AI: Train custom ML models using a graphical interface with minimal coding.

Answer

Cloud AutoML: End-to-end ML platform for building, training, and deploying models.

Answer

Vision API: Analyze text for sentiment, entity recognition, and syntax.

Question 5

Match each Google Cloud storage option to its best use case.

Accepted Answer

Cloud Storage: Storing unstructured data like images, videos, and documents. Correct matches: A (Cloud Storage for unstructured data), C (Bigtable for low-latency analytical workloads), E (BigQuery for serverless data warehousing). Incorrect options: B (Cloud Storage lacks transactional consistency for structured data), D (Bigtable is NoSQL, not relational), F (BigQuery is for analytical queries, not real-time transaction processing).

Answer

Cloud Storage: Storing structured transactional data with strong consistency

Answer

Bigtable: Storing relational data with complex joins and transactions

Answer

BigQuery: Real-time transaction processing for small, frequently updated datasets

Question 6

Match each optimization algorithm to its characteristic.

Accepted Answer

SGD: Uses a constant learning rate for all parameters. The correct matches are: A (SGD uses a constant learning rate for all parameters), B (Adam combines momentum and RMSProp with bias correction), and C (RMSProp uses exponential moving average of squared gradients). Option D is incorrect because Adam does not use only momentum; it also includes adaptive learning rates. Option E is incorrect because SGD uses a fixed learning rate, not adaptive.

Answer

Adam: Uses only momentum

Answer

SGD: Uses adaptive learning rate

Question 7

Match each ML acronym to its definition.

Accepted Answer

ML: A subset of AI that enables systems to learn from data.. The correct matches are A (ML), B (AI), C (NLP), and D (CNN). Options E and F are incorrect because they swap definitions: E assigns the definition of AI to ML, and F assigns the definition of CNN to NLP.

Answer

ML: The simulation of human intelligence by machines.

Answer

NLP: A type of neural network designed for processing grid-like data, especially images.

Question 8

Match each feature engineering technique to its description.

Accepted Answer

Polynomial Features: Creates new features as powers of existing features.. This matching question tests your understanding of common feature engineering techniques. Polynomial Features create new features by raising existing features to a power (e.g., x², x³). One-Hot Encoding converts categorical variables into binary columns (0/1) for each category. Feature Scaling standardizes numerical features to have zero mean and unit variance (z-score normalization). Options D and E are incorrect: D confuses Polynomial Features with Label Encoding (which assigns numerical codes), and E confuses One-Hot Encoding with Polynomial Features.

Answer

Polynomial Features: Converts categorical variables into numerical codes.

Answer

One-Hot Encoding: Creates polynomial combinations of features.

Question 9

Match each regularization technique to its effect.

Accepted Answer

L1 Regularization: Encourages sparsity by setting some coefficients to zero. This question matches regularization techniques to their effects. L1 regularization (option A) encourages sparsity by setting some coefficients to zero, which is correct. Option E incorrectly states L1 shrinks coefficients uniformly; that is L2 regularization. L2 regularization (option B) shrinks coefficients to prevent overfitting, which is correct. Dropout (option C) randomly drops neurons during training to prevent co-adaptation, which is correct. Option F incorrectly states dropout adds penalty to the loss function; that describes regularization like L1/L2. Early stopping (option D) halts training when validation performance stops improving, which is correct.

Answer

L1 Regularization: Shrinks coefficients uniformly

Answer

Dropout: Adds penalty to the loss function

Question 10

Match each ML model interpretability method to its description.

Accepted Answer

LIME: Local interpretable model-agnostic explanations by approximating locally with a simpler model.. LIME (Local Interpretable Model-agnostic Explanations) provides local explanations by approximating the model's behavior near a specific prediction using a simpler interpretable model. SHAP (SHapley Additive exPlanations) uses Shapley values from game theory to fairly distribute feature contributions for individual predictions. Partial Dependence Plots (PDP) show the average marginal effect of one or two features on the predicted outcome across the dataset, making it a global method. Permutation Feature Importance measures the increase in prediction error when a feature's values are randomly shuffled, indicating feature importance globally. Common mistakes: confusing LIME with a global method (B) and misattributing SHAP to visualizing decision trees (D).

Answer

LIME: Global interpretability method that provides feature importance rankings across entire dataset.

Answer

SHAP: A method for visualizing individual decision trees.