DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 financial services company is building a real-time fraud detection system. Transactions are streamed from multiple sources into Azure Event Hubs. The system must run a trained machine learning model (scored in near real-time) to flag suspicious transactions. The model is a Python pickle file that needs to be deployed as a web service with low latency (under 100 ms per prediction). The data engineering team wants to use a serverless compute option to run the scoring logic, and the solution must integrate with Azure Stream Analytics for alerting. Which Azure service should you use to deploy the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Machine Learning managed online endpoint
Azure Machine Learning managed online endpoints are the correct choice because they are designed for deploying trained models (including Python pickle files) as low-latency web services (under 100 ms per prediction) with serverless compute. They natively integrate with Azure Stream Analytics for alerting, allowing real-time scoring of streaming transactions from Event Hubs without managing infrastructure.
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.
✗
Azure Functions
Why it's wrong here
Functions can host models but are not optimized for ML serving; cold starts may add latency.
✓
Azure Machine Learning managed online endpoint
Why this is correct
Managed endpoints are serverless and provide low-latency inference APIs, easily integrated with Azure Stream Analytics.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Kubernetes Service (AKS)
Why it's wrong here
AKS is not serverless and requires cluster management.
✗
Azure Databricks
Why it's wrong here
Databricks is better for batch inference, not low-latency real-time APIs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Azure Functions because it is serverless and familiar, but they overlook the strict latency requirement (under 100 ms) and the need for native integration with Azure Stream Analytics, which Azure Machine Learning managed online endpoints satisfy directly.
Detailed technical explanation
How to think about this question
Azure Machine Learning managed online endpoints automatically handle scaling, load balancing, and health monitoring, using a containerized deployment that can serve predictions in under 100 ms by leveraging optimized inference runtimes (e.g., ONNX Runtime or TensorFlow Serving). The integration with Azure Stream Analytics works via a custom output sink that calls the endpoint's REST API for each event, enabling real-time alerting on flagged transactions. A subtle behavior is that the endpoint must be deployed with a scoring script that deserializes the pickle file and processes input data in the exact format expected by the model, which can be a common source of integration errors.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Machine Learning managed online endpoint — Azure Machine Learning managed online endpoints are the correct choice because they are designed for deploying trained models (including Python pickle files) as low-latency web services (under 100 ms per prediction) with serverless compute. They natively integrate with Azure Stream Analytics for alerting, allowing real-time scoring of streaming transactions from Event Hubs without managing infrastructure.
What should I do if I get this DP-900 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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