A data science team uses Azure Databricks for model training. They need to automate the training pipeline and integrate with Azure DevOps for CI/CD. The pipeline must also deploy the trained model to Azure Machine Learning for scoring. Which approach should you recommend?

Use Azure Machine Learning pipelines with Databricks as a compute target.

Use Azure Logic Apps to trigger Databricks jobs and deploy models.

Use Azure Databricks notebooks with the Databricks CLI and Azure DevOps to automate the pipeline, and register the model in Azure ML.

Use Azure Data Factory to run Databricks notebooks and deploy the model to Azure ML.

A company uses Azure Data Factory to load data from a REST API into Azure Synapse Analytics. The API has a rate limit of 100 requests per minute. The pipeline currently makes one request per row, which takes too long and sometimes hits the rate limit. The API supports pagination and filtering. What should you do to improve performance and avoid rate limiting?

Use the REST connector's pagination rules to fetch multiple rows per request and use a filter to reduce the data volume.

Use Azure Logic Apps to call the API and then load the data into Synapse.

Increase the number of concurrent copy activities to speed up the process.

Use a Web activity to call the API and then parse the response in a Data Flow.

Azure Solutions Architect Expert

Domain 2Objective 3

Design Data Integration AZ-305 Practice Questions (Page 6)

Part of the Design data storage solutions domain, which makes up ~25% of our current practice bank. Microsoft does not publish an official question count, but from its 120-minute exam (~50–80 total, ~13–20 in this domain), expect 4–7 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

7concepts

Questions 26–28

26expert · hard
A data science team uses Azure Databricks for model training. They need to automate the training pipeline and integrate with Azure DevOps for CI/CD. The pipeline must also deploy the trained model to Azure Machine Learning for scoring. Which approach should you recommend?
Select an answer first
27expert · hard
A company uses Azure Data Factory to load data from a REST API into Azure Synapse Analytics. The API has a rate limit of 100 requests per minute. The pipeline currently makes one request per row, which takes too long and sometimes hits the rate limit. The API supports pagination and filtering. What should you do to improve performance and avoid rate limiting?
Select an answer first
28foundation · easy
A company uses Azure Synapse Analytics to store and query their sales data. They want to create interactive dashboards and reports that can be shared with business users. Which Azure service should they integrate with Synapse Analytics to build these visualizations?
Select an answer first

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Design Data Integration AZ-305 Practice Questions (Page 6)

Questions 26–28

A data science team uses Azure Databricks for model training. They need to automate the training pipeline and integrate with Azure DevOps for CI/CD. The pipeline must also deploy the trained model to Azure Machine Learning for scoring. Which approach should you recommend?

A company uses Azure Synapse Analytics to store and query their sales data. They want to create interactive dashboards and reports that can be shared with business users. Which Azure service should they integrate with Synapse Analytics to build these visualizations?

Keep studying — Design data storage solutions