AIF-C01 Fundamentals of AI and ML Practice Question
A financial services company is preparing to train a machine learning model on customer transaction data. The data science team must address data quality concerns before training, because poor data directly harms model performance. Which TWO practices best improve the quality of the training data? (Choose two.)
⚠ Common exam trap
The trap here is treating model tuning knobs or dataset inflation as data quality fixes when quality work targets the data itself.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Handle missing values by imputing them or removing affected records after analysis
Improving data quality before training centers on correcting defects in the data itself. Handling missing values prevents bias and instability, while scaling numeric features ensures algorithms treat inputs comparably. Hyperparameter changes, row duplication, and deleting predictive features do not repair data defects and in several cases worsen results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more duplicate rows to increase the effective dataset size
Why it's wrong here
Duplicating rows inflates the dataset without adding information and can cause the model to overfit to repeated examples. It also distorts class balance if duplicates concentrate in one category. Duplication is the opposite of quality improvement, because it amplifies existing patterns and noise rather than correcting data defects.
- ✗
Increase the learning rate so the model converges faster on noisy data
Why it's wrong here
Learning rate is a training hyperparameter, not a data quality remedy. Raising it can cause divergence or oscillation, especially with noisy data, and it does nothing to fix missing values, outliers, or inconsistent scales. Treating a training setting as a data cleaning step confuses model tuning with data preparation.
- ✓
Handle missing values by imputing them or removing affected records after analysis
Why this is correct
Missing values can bias or destabilize training if left untreated. Analyzing the pattern of missingness and then imputing sensible values or removing affected records produces a cleaner dataset that better represents the population. This is a foundational data quality practice directly tied to the goal of improving model performance before training begins.
- ✗
Remove all features that have any correlation with the target variable
Why it's wrong here
Features correlated with the target are often the most predictive signals, so removing them would strip useful information and degrade performance. Correlation does not imply a data quality problem; it frequently indicates relevance. This practice would actively harm the model and misapplies the concept of feature selection.
- ✓
Normalize or standardize numeric features so they share a comparable scale
Why this is correct
Features on wildly different scales, such as transaction amount versus transaction count, can dominate distance-based and gradient-based algorithms. Scaling them to a comparable range improves convergence and prevents one feature from disproportionately influencing the model. This is a standard preprocessing practice that raises effective data quality for training.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.