The answer is that the model has high precision but low recall, so it misses many failures. This is correct because precision measures the proportion of positive identifications that were actually correct, meaning when the model flags a failure, it is very reliable; recall, on the other hand, measures the proportion of actual failures that were correctly identified, so low recall indicates the model fails to catch a significant number of real equipment failures. On the CompTIA Data+ DA0-001 exam, this question tests your ability to communicate model performance trade-offs clearly to a non-technical audience, avoiding the common trap of focusing solely on high accuracy, which can be misleading when classes are imbalanced. A useful memory tip is to think of precision as "promise" (when it says failure, it's true) and recall as "reality" (how many real failures it actually finds).
DA0-001 Communicating Data Insights Practice Question
This DA0-001 practice question tests your understanding of communicating data insights. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 team is communicating findings from a machine learning model that predicts equipment failure. The model has high accuracy but low recall. Which of the following statements is the most accurate way to communicate the model's performance to the maintenance team?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
"The model has a high precision but low recall, so it misses many failures."
Option C is correct because it directly states that the model has high precision but low recall, which means that when the model predicts a failure, it is likely correct (few false positives), but it fails to identify many actual failures (many false negatives). This is the most accurate way to communicate the trade-off to the maintenance team, as it clearly indicates that the model will miss some failures despite its high accuracy.
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.
✗
"The model has a high precision, so when it alerts, it is usually correct, but it may miss some failures."
Why it's wrong here
Incorrect. 'May miss some' downplays the severity of low recall; it misses many.
✗
"The model rarely misses a failure, but may have false positives."
Why it's wrong here
Incorrect. Low recall means it misses many failures; this statement describes high recall.
✓
"The model has a high precision but low recall, so it misses many failures."
Why this is correct
Correct. This accurately communicates the trade-off between precision and recall.
Related concept
Read the scenario before looking for a memorised answer.
✗
"The model is highly reliable and catches almost all failures."
Why it's wrong here
Incorrect. Low recall means it misses many failures, so it does not catch almost all.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the confusion between accuracy and recall; candidates mistakenly assume high accuracy implies high recall, but accuracy can be high even with low recall if the class imbalance is severe (e.g., many non-failure cases dominate the metric).
Detailed technical explanation
How to think about this question
In classification metrics, recall (sensitivity) is calculated as TP/(TP+FN), and low recall indicates a high number of false negatives. For equipment failure prediction, a low recall model might miss critical failures, leading to unplanned downtime, even if its precision (TP/(TP+FP)) is high. This trade-off is often managed by adjusting the decision threshold or using cost-sensitive learning to prioritize recall over precision in safety-critical applications.
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 practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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.
Communicating Data Insights — This question tests Communicating Data Insights — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: "The model has a high precision but low recall, so it misses many failures." — Option C is correct because it directly states that the model has high precision but low recall, which means that when the model predicts a failure, it is likely correct (few false positives), but it fails to identify many actual failures (many false negatives). This is the most accurate way to communicate the trade-off to the maintenance team, as it clearly indicates that the model will miss some failures despite its high accuracy.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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