DP-900 Describe an analytics workload on Azure Practice Question
A data scientist needs to perform exploratory data analysis on a large dataset stored in Azure Data Lake Storage Gen2 using Python notebooks. The solution must minimize infrastructure management. Which Azure service should the data scientist use?
⚠ Common exam trap
It's easy for candidates to confuse Azure Machine Learning compute instances (which are for ML model development) with a general-purpose data analytics environment, or they assume HDInsight's Jupyter notebooks are equally managed, overlooking the significant infrastructure management overhead and lack of serverless autoscaling.
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
✓
Azure Databricks with collaborative notebooks.
Azure Databricks provides a fully managed, collaborative notebook environment optimized for big data analytics and machine learning. It integrates natively with Azure Data Lake Storage Gen2, allowing the data scientist to perform exploratory data analysis (EDA) using Python notebooks without managing any underlying infrastructure. This minimizes operational overhead while providing autoscaling clusters and built-in Spark capabilities.
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 Machine Learning compute instances.
Why it's wrong here
Azure Machine Learning compute instances are virtual machines pre-configured for machine learning experimentation, primarily serving model training, hyperparameter tuning, and deployment workflows. Although these instances can run Jupyter notebooks, they lack the serverless, auto-scaling Spark architecture needed for interactive exploratory analysis of large data volumes; each compute instance is a single VM that must be provisioned and manually managed, and it does not provide a shared, collaborative notebook environment for the entire data science team. Thus, the service is designed around the ML lifecycle, not as a general-purpose EDA platform.
- ✗
Power BI with dataflows.
Why it's wrong here
Power BI with dataflows is a business intelligence and data preparation tool built on Power Query M, designed to shape data into analytical models for dashboards and reports. Dataflows do allow self-service data preparation, but they are not a Python/R-based interactive environment and lack the statistical and machine-learning libraries needed for exploratory data science, nor do they support running ad-hoc queries over large, distributed datasets. Power BI is focused on visualization and sharing insights, not on open-ended exploration by data scientists.
- ✗
Azure HDInsight with Jupyter notebooks.
Why it's wrong here
Azure HDInsight with Jupyter notebooks does provide Spark clusters and notebook support, but it requires upfront provisioning and ongoing management of persistent cluster resources. Unlike Databricks' serverless offering, HDInsight forces you to size, configure, and scale clusters manually, and you pay for the cluster even during idle periods; also, the notebook experience is less integrated and collaborative than Databricks, with no built-in collaborative sharing or versioning. The operational overhead makes it a heavier choice for simple, fast data exploration.
- ✓
Azure Databricks with collaborative notebooks.
Why this is correct
Azure Databricks with collaborative notebooks is the correct choice because it provides a serverless, auto-scaling Apache Spark platform purpose-built for interactive data exploration and data science. Notebooks support Python, R, SQL, and Scala, and allow multiple data scientists to share and co-edit in real time with integrated version control, while the cluster can auto-start, scale, and terminate to minimize cost. Databricks also includes built-in data visualization and integration with the lakehouse architecture, making it ideal for EDA.
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Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
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This DP-900 practice question is part of Courseiva's free Microsoft 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 DP-900 exam.