- A
Handle missing values by imputing or removing them.
Missing values can cause errors or bias; imputation or removal is a standard practice.
- B
Split the data into training, validation, and test sets.
This allows model evaluation on unseen data and helps detect overfitting.
- C
Remove all outliers to improve model robustness.
Why wrong: Removing all outliers may discard important information; domain knowledge should guide outlier handling.
- D
Use the entire dataset for training to maximize data usage.
Why wrong: Using all data for training leaves no data for evaluation, risking overfitting and poor generalization.
- E
Avoid shuffling the data to preserve original order.
Why wrong: Shuffling is important to avoid order bias, especially when using stochastic gradient descent.
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are best practices for preparing training data for a machine learning model?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 or removing them.
Option A is correct because handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Handle missing values by imputing or removing them.
Why this is correct
Missing values can cause errors or bias; imputation or removal is a standard practice.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Split the data into training, validation, and test sets.
Why this is correct
This allows model evaluation on unseen data and helps detect overfitting.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all outliers to improve model robustness.
Why it's wrong here
Removing all outliers may discard important information; domain knowledge should guide outlier handling.
- ✗
Use the entire dataset for training to maximize data usage.
Why it's wrong here
Using all data for training leaves no data for evaluation, risking overfitting and poor generalization.
- ✗
Avoid shuffling the data to preserve original order.
Why it's wrong here
Shuffling is important to avoid order bias, especially when using stochastic gradient descent.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that removing all outliers is always beneficial, when in fact domain knowledge is required to distinguish between noise and legitimate extreme values that may be critical for model accuracy.
Detailed technical explanation
How to think about this question
In practice, the choice of imputation method matters: for numerical features, mean/median imputation assumes data is missing completely at random (MCAR), while for categorical data, mode imputation is common. More advanced techniques like KNN imputation or using a separate model to predict missing values can preserve relationships. The split into training, validation, and test sets (e.g., 70/15/15) is fundamental for hyperparameter tuning and unbiased final evaluation, with the validation set used for model selection and the test set for final performance reporting.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Handle missing values by imputing or removing them. — Option A is correct because handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
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.
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