- A
Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.
Why wrong: Discarding data reduces sample size and may introduce bias.
- B
Ignore calibration because AUC is the only metric that matters for readmission risk models.
Why wrong: Calibration is important for decision-making; AUC alone can be misleading.
- C
Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.
Platt scaling is designed to improve calibration while maintaining AUC.
- D
Switch to a random forest model, which inherently produces better-calibrated probabilities.
Why wrong: Random forests often have better calibration but may not necessarily preserve AUC; also, this changes the model entirely.
Quick Answer
The answer is to apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model. This is correct because Platt scaling is a post-processing technique that fits a new logistic regression model using the original model’s output scores as the sole feature, trained on a separate validation set to map those scores to well-calibrated probabilities. Crucially, because it is a monotonic transformation, it preserves the rank order of predictions, meaning the AUC—a measure of discrimination—remains unchanged while directly fixing the miscalibration issue described. On the CompTIA Data+ DA0-001 exam, this question tests your understanding that calibration and discrimination are distinct properties; a common trap is thinking recalibration requires retraining the original model or that it will hurt ranking ability. Remember the memory tip: Platt scaling is a “monotonic makeover”—it reshapes probabilities without reshuffling the order.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?
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
Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.
Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.
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.
- ✗
Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.
Why it's wrong here
Discarding data reduces sample size and may introduce bias.
- ✗
Ignore calibration because AUC is the only metric that matters for readmission risk models.
Why it's wrong here
Calibration is important for decision-making; AUC alone can be misleading.
- ✓
Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.
Why this is correct
Platt scaling is designed to improve calibration while maintaining AUC.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a random forest model, which inherently produces better-calibrated probabilities.
Why it's wrong here
Random forests often have better calibration but may not necessarily preserve AUC; also, this changes the model entirely.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think Platt scaling changes the model's ranking (AUC), but in reality it applies a monotonic transformation that preserves rank order, so discrimination is unaffected.
Detailed technical explanation
How to think about this question
Platt scaling works by treating the original model's logit scores as input features to a new logistic regression model trained on a separate validation set, effectively learning a monotonic transformation that maps raw scores to well-calibrated probabilities. This method preserves the AUC because the transformation is monotonic, meaning the relative order of predictions remains unchanged. In real-world healthcare deployments, miscalibrated probabilities can lead to incorrect triage decisions, such as over-allocating resources to low-risk patients or under-identifying high-risk patients.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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.
- →
Analyzing and Modeling Data — study guide chapter
Learn the concepts, then practise the questions
- →
Analyzing and Modeling Data practice questions
Targeted practice on this topic area only
- →
All DA0-001 questions
509 questions across all exam domains
- →
CompTIA Data+ DA0-001 study guide
Full concept coverage aligned to exam objectives
- →
DA0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DA0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Comparing and Contrasting Data Concepts practice questions
Practise DA0-001 questions linked to Comparing and Contrasting Data Concepts.
Mining and Acquiring Data practice questions
Practise DA0-001 questions linked to Mining and Acquiring Data.
Analyzing and Modeling Data practice questions
Practise DA0-001 questions linked to Analyzing and Modeling Data.
Visualizing Data practice questions
Practise DA0-001 questions linked to Visualizing Data.
Communicating Data Insights practice questions
Practise DA0-001 questions linked to Communicating Data Insights.
CompTIA A+ hardware practice questions
Practise DA0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise DA0-001 questions linked to CompTIA A+ mobile devices.
CompTIA A+ networking practice questions
Practise DA0-001 questions linked to CompTIA A+ networking.
CompTIA A+ operating systems practice questions
Practise DA0-001 questions linked to CompTIA A+ operating systems.
CompTIA A+ security practice questions
Practise DA0-001 questions linked to CompTIA A+ security.
CompTIA A+ software troubleshooting questions
Practise DA0-001 questions linked to CompTIA A+ software troubleshooting questions.
CompTIA A+ operational procedures questions
Practise DA0-001 questions linked to CompTIA A+ operational procedures questions.
Practice this exam
Start a free DA0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this DA0-001 question test?
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model. — Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.
What should I do if I get this DA0-001 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More DA0-001 practice questions
- Drag and drop the steps to clean a dataset with missing values in the correct order.
- Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.
- Drag and drop the steps to create a data visualization dashboard in the correct order.
- Drag and drop the steps to implement a data classification policy in the correct order.
- Drag and drop the steps for the ETL (Extract, Transform, Load) process in the correct order.
- Drag and drop the steps to perform a data backup using the 3-2-1 rule in the correct order.
Last reviewed: Jun 11, 2026
This DA0-001 practice question is part of Courseiva's free CompTIA 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 DA0-001 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.