5 exercises — essential vocabulary for data scientists, ML engineers, and analysts: model evaluation metrics, pipeline terminology, and the concepts you need to discuss AI systems in English.
Core ML vocabulary clusters
Model quality: overfitting, underfitting, generalisation, bias-variance trade-off
Evaluation: accuracy, precision, recall, F1 score, AUC-ROC, confusion matrix
A data scientist explains their work to a colleague. Which sentence correctly describes overfitting?
Overfitting means the model has learned the training data too well — it has memorised the noise and specific examples rather than generalising the underlying patterns. Result: excellent training accuracy, poor performance on test/production data. The opposite is underfitting: the model is too simple to capture patterns in the training data (option A describes this). Option C confuses overfitting with a data volume problem — overfitting is about model complexity vs. data, not data size per se. Option D partially relates (too many epochs can cause overfitting) but isn't the definition. Key vocabulary cluster: overfitting / underfitting → generalisation → train/validation/test split → cross-validation → regularisation (L1/L2) → dropout → early stopping. In practice: "The model achieved 98% accuracy on training data but only 72% on the test set — a clear sign of overfitting."
2 / 15
A team is evaluating a binary classifier for detecting fraudulent transactions. They note: "Our model flags very few legitimate transactions as fraud." Which metric does this statement describe?
Precision measures how many of the model's positive predictions are actually correct. "Few legitimate transactions flagged as fraud" = few false positives = high precision. Formula: Precision = TP / (TP + FP). Recall (option A) measures how many of the actual positives the model caught — "how many real fraud cases did we find?". Formula: Recall = TP / (TP + FN). The precision/recall trade-off is crucial in fraud detection: high precision = low false alarm rate; high recall = finding more real fraud but more false alarms. F1 score (option C) is the harmonic mean of precision and recall — useful when both matter. Accuracy (option D) = (TP + TN) / total — misleading when classes are imbalanced (e.g., 99% of transactions are legitimate — a model that always says "not fraud" gets 99% accuracy but 0% recall). Practical example: "We want high precision for the fraud alert system — false alarms cause customer frustration."
3 / 15
An ML engineer describes a system component: "This is the sequence of automated steps that takes raw data, transforms it, and produces model predictions at scale." What is the correct technical term?
An ML pipeline is an automated, end-to-end workflow that processes data and produces predictions. A typical ML pipeline includes: data ingestion → preprocessing → feature engineering → model training → evaluation → deployment → monitoring. "Pipeline" is used across many IT contexts but in ML/data engineering it specifically refers to this sequential, automated processing chain. The other terms: Data warehouse (option A) — a structured, query-optimised storage system for historical business data (Snowflake, BigQuery, Redshift). Feature store (option B) — a centralised repository for ML features, enabling reuse and consistency across models (Feast, Tecton, Hopsworks). Data lake (option D) — raw, unstructured or semi-structured data storage at scale (S3, ADLS, GCS). Key related terms: batch pipeline (processes data in chunks), streaming pipeline (processes data in real-time), ETL (Extract, Transform, Load), feature engineering (creating input variables from raw data).
4 / 15
A data scientist says: "We need to tune the learning rate, batch size, and regularisation strength before training." What are these parameters called?
Hyperparameters are settings configured before training begins — they control the learning process itself, not the learned patterns. Examples: learning rate, batch size, number of epochs, regularisation strength (λ), number of layers, dropout rate, kernel size. Model parameters (option A) are learned during training — the weights and biases in a neural network, the coefficients in linear regression. You don't set model parameters manually; the training algorithm finds them. Feature weights (option C) — a common term for model coefficients in linear models, but not the general term for pre-training settings. Training labels (option D) — the ground truth output values (y) in supervised learning. Hyperparameter tuning methods: grid search (try all combinations), random search (sample randomly), Bayesian optimisation, AutoML. Example: "After hyperparameter tuning with a learning rate of 0.001 and batch size 64, validation accuracy improved by 4%."
5 / 15
A data analyst reads this in a project requirement: "The model must explain which features contributed most to each prediction." Which concept does this requirement describe?
Model explainability (or interpretability) is the ability to understand and communicate why a model made a specific prediction. This is critical in high-stakes domains: healthcare, finance, hiring, and security — where decisions must be auditable and justifiable. Key explainability tools and methods: SHAP (SHapley Additive exPlanations) — assigns each feature a contribution to the prediction. LIME (Local Interpretable Model-Agnostic Explanations) — approximates the model locally with a simpler interpretable model. Feature importance — which features most influence predictions globally (XGBoost, Random Forest built-in). Attention weights — in transformer models, which input tokens the model attended to. Feature scaling (option C): normalising/standardising input features (min-max, z-score) — not the same as explaining predictions. Cross-validation (option D): technique to reliably estimate model performance using multiple train/test splits. In conversation: "The client needs explainability — they won't accept a black box for loan decisions."
6 / 15
Code Review Comment: Sarah (Senior Data Scientist) comments on a colleague's PR:
'This feature uses a simple linear regression. While it's functional, consider adding more robust error handling and exploring techniques like polynomial features to capture non-linear relationships.'
Which of the following best describes Sarah's suggestion regarding 'polynomial features'?
Sarah isn't simply suggesting a different statistical model. She's pointing out that linear regression might not be the best fit due to potential non-linear relationships in the data. 'Polynomial features' are a technique for adding new features derived from existing ones (e.g., squaring or cubing variables) which can allow a linear model to capture more complex patterns. This corrects the likely misconception of only considering different models.
7 / 15
Slack Message: Alex (ML Engineer) sends a message to the team:
'Just ran some A/B tests on the new fraud detection model. Initial results show a slight lift in precision – about 2% better than the baseline, but recall is still quite low at 70%. We need to investigate further.'
What does Alex primarily highlight in this message?
Alex focuses on the trade-off between precision and recall. A high precision means fewer false positives but a low recall indicates that many fraudulent transactions are being missed. The message reveals the need to improve recall – the ability of the model to correctly identify all fraudulent transactions – which is crucial for fraud detection.
8 / 15
PR Description: You are writing the description for a pull request that implements a new feature in your model pipeline. You want to accurately describe the process:
'This PR introduces a component that automatically processes unstructured text data, transforming it into numerical features suitable for downstream machine learning models.'
Which technical term best describes this automated transformation?
'Dimensionality reduction' refers to techniques like Principal Component Analysis (PCA) or other methods that transform high-dimensional data into a lower-dimensional space while retaining important information. While 'feature engineering' is related, it's broader. 'Text vectorization' is simply the *process* of converting text into numbers; dimensionality reduction is the specific technique used to achieve this efficiently.
9 / 15
Standup Update: David (Data Analyst) reports:
'I've been working on calculating the churn rate for our subscription service. We're seeing a significant increase in churn among users who haven't logged in for 30 days.'
Which of the following best describes David's primary focus?
David is directly investigating churn – a critical business metric. His statement highlights a specific segment (users inactive for 30 days) and the goal of understanding *why* customers are leaving. 'Predicting future customer behavior' is a broader application of data analysis; the question specifically asks about root causes.
10 / 15
During a discussion about model evaluation, Maria (Data Scientist) says: 'We need to ensure our model generalizes well to unseen data.' Which concept is Maria referring to?
Generalization refers to a model's capacity to perform accurately on data it hasn't been trained on – a key indicator of its true predictive power. Option A describes performance on training data only; option B focuses on robustness; and option D relates to computational cost.
11 / 15
Mark (Data Scientist) is reviewing a colleague's code for a new feature that predicts customer lifetime value. He comments: 'I'm concerned about the potential for data leakage here. The model is using future purchase dates to predict current spending.' What does Mark most likely mean?
Mark is highlighting a critical issue: data leakage. This occurs when the model uses information that would not be available at prediction time – in this case, future purchase dates. This leads to an overly optimistic and unrealistic assessment of the model's performance on unseen data. Options A, C, and D are all plausible scenarios but don't represent the core problem of using future information.
12 / 15
Emily (ML Engineer) is explaining a new deployment strategy to her team. She says: 'We're implementing canary deployments, rolling out the updated model to a small subset of users before full release.' What's the primary benefit of this approach?
Canary deployments are a risk mitigation technique. By releasing the updated model to a limited group of users first, the team can quickly identify and address any issues – bugs or performance problems – before they affect all users. This provides a 'safety net' for deploying new models with minimal disruption.
13 / 15
During a standup meeting, John (Data Analyst) says: 'I've been exploring different feature engineering techniques to improve the model's predictive power.' Which of the following best describes his activity?
Feature engineering involves manipulating and creating new features from existing ones to enhance a machine learning model's ability to learn. It's a crucial step in the ML pipeline that often significantly impacts model accuracy. Options A, C, and D describe other data preparation or model building activities.
14 / 15
Liam (ML Engineer) is writing a pull request description for a new component that calculates the F1 score. He wants to be precise. Which statement best describes the purpose of calculating the F1 score?
The F1 score is a harmonic mean of precision and recall. It's particularly useful when dealing with imbalanced datasets where optimizing solely for accuracy can be misleading. Calculating the F1 score offers a balanced view of the model's performance by considering both false positives (precision) and false negatives (recall).
15 / 15
Sarah (Data Scientist) is discussing the concept of 'drift' with her team. She says: 'We need to monitor for model drift – changes in the input data distribution that could degrade our model's performance.' What does Sarah mean?
Model drift refers to the phenomenon where the statistical properties of the input data change over time. This can happen due to various factors (e.g., seasonality, changing customer behavior). When the training data no longer matches the production data, the model's performance degrades, necessitating retraining or adaptation.
What does the "Data Science & ML Vocabulary" vocabulary exercise cover?
This exercise tests real IT vocabulary related to data science & ml vocabulary through 15 multiple-choice questions, each built from realistic workplace sentences rather than abstract definitions.
Is this vocabulary exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is completely free — no account, sign-up, or payment required.
How many questions does this exercise have?
This exercise has 15 questions. Each one shows a real-world sentence or scenario with multiple-choice options and an explanation once you answer.
What happens after I answer a question?
You'll see immediate feedback showing whether your answer was correct, along with a short explanation of why — then a button to move to the next question, and a full results screen at the end.
Can I retry the exercise if I get questions wrong?
Yes. Once you reach the results screen, click "Try again" to reset your answers and go through the exercise from the start as many times as you like.
Do I need to create an account to take this exercise?
No account is needed. Your answers are scored in your browser during the session — nothing is saved to a server, so you can jump straight in.
Is my progress saved if I leave the page?
No — progress within an exercise resets if you navigate away or reload. Each exercise is short enough to complete in a few minutes in one sitting.
Are these vocabulary exercises connected to other topics?
Yes — browse the full vocabulary exercises hub to find related modules covering adjacent IT topics and roles.
How is this different from reading a glossary or blog article?
Exercises like this one are active recall drills — you have to choose the correct term or phrasing yourself, which builds retention faster than passively reading a definition.
Where can I find more vocabulary exercises?
Browse the full Vocabulary exercises hub for hundreds of modules covering Agile, DevOps, security, databases, architecture, and more — organised by IT role and skill.