The answer is Data Collection, as it initiates the AI/ML network operations workflow by gathering telemetry and metrics from Cisco IOS-XE devices. This step is foundational because without accurate, real-time data from interfaces, CPU, or memory, subsequent streaming, model training, and deployment cannot produce reliable anomaly detection or automated remediation. On the CCNA 200-301 v2 exam, this drag-and-drop sequence tests your understanding of the logical progression from raw data to actionable automation—a common trap is placing model training before streaming, but streaming must first transport the collected data to a processing engine. Remember the mnemonic “Data Streams Train Deploy Fix” to recall the order: Data Collection, Streaming, Model Training, Deployment, then Automated Remediation.
CCNA AI and Network Operations Practice Question
This 200-301 practice question tests your understanding of ai and network operations. 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.
Drag and drop the following steps into the correct order to implement an AI/ML-based network operations workflow for proactive anomaly detection and automated remediation on a Cisco IOS-XE device.
This is correct because the first step in any AI/ML workflow is to collect data from network devices, such as telemetry data, logs, and metrics, which forms the foundation for analysis.
B
Model Training
Why wrong: This is incorrect because model training occurs after data collection and streaming, not as the first step. The model needs data to learn from.
C
Automated Remediation
Why wrong: This is incorrect because automated remediation is the final step, triggered only after the model detects an anomaly. It cannot occur before data collection and analysis.
D
Data Streaming
Why wrong: This is incorrect because data streaming comes after data collection, not before. Data must be collected before it can be streamed to a central system.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data Collection
The workflow starts with data collection, then streaming, model training, deployment, and finally automated remediation based on detection.
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.
✓
Data Collection
Why this is correct
This is correct because the first step in any AI/ML workflow is to collect data from network devices, such as telemetry data, logs, and metrics, which forms the foundation for analysis.
Related concept
Read the scenario before looking for a memorised answer.
✗
Model Training
Why it's wrong here
This is incorrect because model training occurs after data collection and streaming, not as the first step. The model needs data to learn from.
✗
Automated Remediation
Why it's wrong here
This is incorrect because automated remediation is the final step, triggered only after the model detects an anomaly. It cannot occur before data collection and analysis.
✗
Data Streaming
Why it's wrong here
This is incorrect because data streaming comes after data collection, not before. Data must be collected before it can be streamed to a central system.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The 200-301 exam frequently reuses these exact scenarios with slightly different constraints.
✓Data CollectionCorrect answer▾
Why this is correct
This is correct because the first step in any AI/ML workflow is to collect data from network devices, such as telemetry data, logs, and metrics, which forms the foundation for analysis.
✗Model TrainingWrong answer — click to see why▾
Why this is wrong here
The specific factual error is that model training cannot happen without first collecting and preparing data.
Why candidates choose this
Candidates might think training is the first step because it is a core part of AI/ML, but they overlook the prerequisite of data collection.
✗Automated RemediationWrong answer — click to see why▾
Why this is wrong here
The specific factual error is that remediation is a response action, not an initial step in the workflow.
Why candidates choose this
Candidates might confuse the order because remediation is a key outcome, but they forget it depends on earlier steps.
✗Data StreamingWrong answer — click to see why▾
Why this is wrong here
The specific factual error is that streaming is a transport step that follows collection.
Why candidates choose this
Candidates might think streaming is first because it is often the first visible activity in real-time monitoring, but collection precedes it.
Analysis generated from the official 200-301blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 200-301 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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this 200-301 question in full detail.
Identify which 200-301 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
AI and Network Operations — This question tests AI and Network Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Data Collection — The workflow starts with data collection, then streaming, model training, deployment, and finally automated remediation based on detection.
What should I do if I get this 200-301 question wrong?
Identify which 200-301 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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