- A
Frequency encoding
Frequency encoding replaces categories with their frequency counts, reducing to one column.
- B
One-hot encoding
Why wrong: One-hot encoding increases dimensionality, not reduces.
- C
Dimensionality reduction using PCA
Why wrong: PCA is for numeric features, not directly for categorical high-cardinality features.
- D
Target encoding
Target encoding creates a single numeric column based on target mean.
- E
Label encoding
Why wrong: Label encoding reduces dimensionality but imposes ordinality, which is not always suitable.
Question 563 of 1,000
easyMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is preparing a dataset for a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A data scientist is preparing a dataset for a binary classification model. The dataset has a high-cardinality categorical feature with thousands of unique values. Which TWO techniques can reduce the dimensionality of this feature? (Select TWO.)
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
✓
Frequency encoding
Frequency encoding replaces each category with its count (or frequency) in the dataset, collapsing thousands of unique values into a single numeric column. This reduces dimensionality while preserving the relative popularity of each category, which can be useful for tree-based models.
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.
- ✓
Frequency encoding
Why this is correct
Frequency encoding replaces categories with their frequency counts, reducing to one column.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding increases dimensionality, not reduces.
- ✗
Dimensionality reduction using PCA
Why it's wrong here
PCA is for numeric features, not directly for categorical high-cardinality features.
- ✓
Target encoding
Why this is correct
Target encoding creates a single numeric column based on target mean.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Label encoding
Why it's wrong here
Label encoding reduces dimensionality but imposes ordinality, which is not always suitable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between techniques that reduce the number of columns (dimensionality reduction) versus those that merely transform the representation; candidates mistakenly choose one-hot encoding or label encoding because they change the data format, but they do not reduce the number of features.
Detailed technical explanation
How to think about this question
Frequency encoding works by mapping each category to its occurrence count (or normalized frequency), effectively summarizing the distribution in a single feature. Target encoding, on the other hand, replaces categories with the mean of the target variable for that category, which can capture predictive signal but risks overfitting if not regularized (e.g., using smoothing or cross-validation). In real-world scenarios like user IDs or ZIP codes, these techniques help avoid the curse of dimensionality while retaining useful information.
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 MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Frequency encoding — Frequency encoding replaces each category with its count (or frequency) in the dataset, collapsing thousands of unique values into a single numeric column. This reduces dimensionality while preserving the relative popularity of each category, which can be useful for tree-based models.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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