CAIC Data Analytics 2 — Questions and Answers
Question 1: Which statistical technique is most appropriate for identifying hidden groupings in unlabeled customer data?
- Linear regression
- K-means clustering (Correct answer)
- Logistic regression
- ANOVA
Correct answer: K-means clustering
K-means clustering is an unsupervised technique that partitions data into k groups based on feature similarity without requiring labels.
Question 2: An AI consultant is asked to evaluate model fairness. Which metric detects whether a model performs significantly worse for a protected demographic group?
- R-squared
- Disparate impact ratio (Correct answer)
- Mean absolute error
- Silhouette score
Correct answer: Disparate impact ratio
Disparate impact ratio compares outcome rates across demographic groups and flags values below 0.8 as potentially discriminatory.
Question 3: What does a high variance but low bias in a predictive model typically indicate?
- The model generalizes well
- The model is underfitting
- The model is overfitting (Correct answer)
- The model has balanced error
Correct answer: The model is overfitting
High variance and low bias indicates the model has learned noise in the training data and fails to generalize—a classic overfitting pattern.
Question 4: A data pipeline silently drops records with NULL values in a key field. What type of data quality issue does this represent?
- Data latency
- Schema drift
- Silent data loss (Correct answer)
- Data duplication
Correct answer: Silent data loss
Silent data loss occurs when records are discarded without alerts or logging, making the issue invisible to downstream consumers.
Question 5: Which visualization type is best suited for showing the distribution and spread of a continuous numeric variable?
- Pie chart
- Box plot (Correct answer)
- Stacked bar chart
- Network graph
Correct answer: Box plot
A box plot displays the median, quartiles, and outliers of a continuous variable, making distribution and spread immediately visible.
Question 6: When building an AI recommendation system, which data analytics step ensures the training data reflects current user behavior rather than outdated patterns?
- Feature normalization
- Data freshness validation (Correct answer)
- Dimensionality reduction
- Label smoothing
Correct answer: Data freshness validation
Data freshness validation checks that training data timestamps are recent enough to reflect current behavioral patterns before model training.
Question 7: A model's precision is 0.90 but recall is 0.40 on a fraud detection task. What is the best interpretation?
- The model catches most fraud with few false alarms
- The model misses most fraud cases but rarely flags legitimate transactions as fraud (Correct answer)
- The model is balanced and ready for deployment
- The model has high overall accuracy
Correct answer: The model misses most fraud cases but rarely flags legitimate transactions as fraud
Low recall (0.40) means 60% of actual fraud is missed, while high precision (0.90) means flagged cases are usually correct—the model is too conservative.
CAIC Data Analytics 2
Which statistical technique is most appropriate for identifying hidden groupings in unlabeled customer data?