A retail company uses Azure Stream Analytics to read JSON data from Event Hubs and write to a Delta Lake in Azure Data Lake Storage Gen2. The vendor recently added a new field 'discount_percentage' to the payload, but the Stream Analytics query explicitly lists columns in the SELECT statement. The new field is being ignored. The team wants to automatically include new fields as they appear without rewriting the query each time. What should they do?

Use a JavaScript UDF to parse the JSON and dynamically generate the output schema.

Create a new output to a second Event Hubs namespace and use a separate query for the new field.

Modify the Delta Lake table schema manually to add the new column and restart the job.

Use SELECT * in the query and enable the 'Pass through all fields' option in the output settings.

A gaming company processes player action events from an Event Hub. Each event has a player ID, an action type, and a timestamp. They need to compute the number of actions per player per 1-minute window, but they want to ensure that all events for a single player are processed by the same partition to maintain per-player ordering. What should they do?

Create a separate Event Hub for each player to ensure isolation.

Use a random partition key for each event to distribute load evenly.

In the Stream Analytics query, use the PartitionId function to group by partition.

Set the partition key of the Event Hub to the player ID when sending events.

A company runs an Azure Stream Analytics job that reads from an Event Hub with 32 partitions and writes to a SQL Database. The job is currently using 6 streaming units and is experiencing high latency during peak hours. The input rate is expected to double. They need to reduce latency and handle the increased load. They have a budget constraint and want to avoid over-provisioning. What should they do?

Use a tumbling window of 5 minutes instead of 1 minute to reduce the output frequency.

Reduce the number of Event Hub partitions to 16 to lower the processing overhead.

Keep the streaming units at 6 but increase the batch size of the SQL output.

Increase the streaming units to 12 and enable autoscaling to scale down during off-peak hours.

What is the purpose of watermarking in stream processing?

To compress the data for storage

To partition the data across multiple nodes

To define the maximum delay allowed for late-arriving events

Microsoft Certified:Azure Data Engineer Associate

Domain 2Objective 3

Develop a Stream Processing Solution DP-203 Practice Questions (Page 2)

Part of the Develop data processing domain, which accounts for 40–45% of the DP-203 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~16–29 in this domain), expect 4–7 from this objective — we provide 47 practice questions to prepare you well beyond it. (estimate)

47questions here

10free pages

16concepts

40–45%of the exam

Questions 6–10

6application · medium
A retail company uses Azure Stream Analytics to read JSON data from Event Hubs and write to a Delta Lake in Azure Data Lake Storage Gen2. The vendor recently added a new field 'discount_percentage' to the payload, but the Stream Analytics query explicitly lists columns in the SELECT statement. The new field is being ignored. The team wants to automatically include new fields as they appear without rewriting the query each time. What should they do?
Select an answer first
7application · medium
A gaming company processes player action events from an Event Hub. Each event has a player ID, an action type, and a timestamp. They need to compute the number of actions per player per 1-minute window, but they want to ensure that all events for a single player are processed by the same partition to maintain per-player ordering. What should they do?
Select an answer first
8expert · medium
A company runs an Azure Stream Analytics job that reads from an Event Hub with 32 partitions and writes to a SQL Database. The job is currently using 6 streaming units and is experiencing high latency during peak hours. The input rate is expected to double. They need to reduce latency and handle the increased load. They have a budget constraint and want to avoid over-provisioning. What should they do?
Select an answer first
9foundation · easy
What is the purpose of watermarking in stream processing?
Select an answer first
10foundation · easy
Which tool is commonly used for unit testing data pipelines in Azure Data Factory?
Select an answer first

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Develop a Stream Processing Solution DP-203 Practice Questions (Page 2)

Questions 6–10

What is the purpose of watermarking in stream processing?

Which tool is commonly used for unit testing data pipelines in Azure Data Factory?

Keep studying — Develop data processing