1500 Questions | Azure Data Scientist (DP-100) [2026]

Domain 2: Machine Learning with Azure (39%): The core of the exam; focusing on developing, training, and deploying ML models, as well as implementing Computer Vision and NLP solutions.

Domain 3: Data Analytics with Azure (19%): Leveraging Azure Synapse and Cosmos DB for high-level analytics and building BI reporting solutions.

Domain 4: Data Science with Azure (19%): Utilizing Azure Databricks and Notebooks for experimentation, plus advanced free visualization techniques for decision makers and leaders course using D3.js and Bokeh.

B. Azure Data Lake Storage Gen2

C. Azure File Storage

D. Azure Table Storage

E. Azure Queue Storage

F. Azure Managed Disks

Correct Answer: B

Explanation:

A (Incorrect): This is a relational database and is not cost-effective or performant for petabytes of unstructured data.

C (Incorrect): Azure Files is designed for file shares and lacks the specialized analytics throughput of Data Lake Gen2.

D (Incorrect): Table Storage is a NoSQL key-value store, not ideal for large-scale analytical processing.

E (Incorrect): Queue storage is for messaging between services, not data storage for ML.

F (Incorrect): Managed disks are block storage for VMs and cannot be natively accessed as a shared data lake for distributed analytics.

Question 2: A data scientist needs to deploy a real-time inference endpoint for a trained model. Which Azure Machine Learning compute target provides the best balance of high availability and low latency for production web services?

B. Azure Container Instances (ACI)

C. Azure Kubernetes Service (AKS)

D. Local Docker Container

E. HDInsight Cluster

F. Azure Batch

Correct Answer: C

Explanation:

A (Incorrect): Compute Instances are meant for development and experimentation, not production endpoints.

B (Incorrect): ACI is excellent for testing or low-scale workloads but lacks the high availability and scaling of AKS.

D (Incorrect): Local deployment is only for debugging purposes.

E (Incorrect): HDInsight is for big data processing (Spark/Hadoop), not model inference.

F (Incorrect): Azure Batch is used for high-performance computing and batch jobs, not real-time inference.

Question 3: You are using Azure Databricks to visualize the relationship between two variables. You want to create a highly customized, interactive browser-based visualization that isn't available in the standard Databricks plot library. Which library should you use?

B. Seaborn

C. Bokeh

D. ggplot2

E. Excel

F. Power BI Desktop

Correct Answer: C

Explanation:

A (Incorrect): Matplotlib produces static images rather than interactive browser-based charts.

B (Incorrect): Seaborn is built on top of Matplotlib and is also primarily used for static statistical graphics.

D (Incorrect): While powerful, ggplot2 is a R-based library and less common for Python-centric Databricks interactivity.

E (Incorrect): Excel is a standalone application, not a library for programmatic visualization within a notebook.

F (Incorrect): Power BI is a separate reporting service, not a library used within the Databricks coding environment.

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