DP-203 Practice Exam 56 — 20 Questions

Question 1

You are a data engineer at a healthcare analytics company. The company stores patient records in an Azure Data Lake Storage Gen2 account organized by /patient/{patientId}/year={yyyy}/month={MM}/day={dd}/*.parquet. There are 10,000 patients, and each patient has about 1 GB of data per year. The data is used by data scientists who run ad-hoc queries using Azure Synapse Serverless SQL. They complain that queries scanning multiple patients over the last year take too long and consume too much data. They often need to filter by patientId and a date range. You need to improve query performance and reduce the amount of data scanned. You cannot change the folder structure because it is used by other processes. What should you do?

Accepted Answer

Create external tables in Synapse Serverless SQL that use the folder structure as partitions, and ensure queries filter on year, month, and day.. Option D is correct because creating external tables in Synapse Serverless SQL with the existing folder structure as partitions allows the query engine to perform partition elimination. When queries filter on year, month, and day, Synapse Serverless SQL will only scan the relevant folders, drastically reducing data scanned and improving performance. This approach does not require changing the folder structure, which is used by other processes.

Answer

Reorganize the folder structure to /year={yyyy}/month={MM}/day={dd}/patientId={patientId}/*.parquet.

Answer

Convert the Parquet files to CSV format to improve compression and reduce file size.

Answer

Create views that aggregate data by patient and date, and instruct data scientists to query the views.

Question 2

You are designing a near real-time analytics solution for a retail company. The company has a transactional database in Azure SQL Database that records sales transactions. The data must be available in Azure Synapse Analytics dedicated SQL pool for reporting with less than 15 minutes of latency. The data volume is about 10 GB per day. You need to design the data ingestion pipeline. You also need to ensure that the pipeline can handle schema changes (e.g., new columns added to the source table) without manual intervention. Which approach should you use?

Accepted Answer

Use Azure Data Factory with a change data capture (CDC) mapping data flow to read changes from SQL Database and write to Synapse dedicated SQL pool. Enable schema drift to handle new columns.. Option B is correct because Azure Data Factory's mapping data flows support Change Data Capture (CDC) to incrementally load only changed rows from Azure SQL Database, meeting the <15-minute latency requirement for 10 GB/day. The 'Enable schema drift' option in mapping data flows automatically handles new columns added to the source table without manual intervention, which is essential for schema evolution.

Answer

Use Azure Databricks with Auto Loader to read from SQL Database and write to Synapse using Delta Lake.

Answer

Use Azure Data Share to share the SQL Database tables with Synapse and schedule snapshots every 15 minutes.

Answer

Use Azure Synapse Pipelines with a copy activity to perform a full load every 15 minutes.

Question 3

You are a data engineer at a financial services company. The company uses Azure Synapse Analytics with a dedicated SQL pool for its data warehouse. The current table 'FactTransactions' is 2 TB and uses round-robin distribution. Query performance is poor for queries that frequently filter on 'CustomerID' and join with a 'DimCustomer' table (10 GB, replicated). You need to redesign the table to improve query performance while minimizing data movement during queries. The solution must also support incremental data loading with minimal overhead. You cannot change the storage size limit or add more DWU. What should you do?

Accepted Answer

Recreate the table using hash distribution on CustomerID and use CTAS for incremental loads.. Option D is correct because hash distribution on CustomerID ensures that rows with the same CustomerID are co-located on the same distribution, which eliminates data movement during joins with the replicated DimCustomer table. Using CTAS (CREATE TABLE AS SELECT) for incremental loads allows you to efficiently rebuild the table with minimal overhead by loading only new data into a staging table, then swapping partitions or using CTAS to replace the target table without blocking reads.

Answer

Replicate the FactTransactions table to all distributions.

Answer

Keep round-robin distribution but add indexes on CustomerID and TransactionDate.

Answer

Change distribution to hash on TransactionDate and partition by month.

Question 4

You are a data engineer for a global e-commerce company. You need to design a data processing solution using Azure Databricks that processes real-time clickstream data from Azure Event Hubs. The solution must join the streaming data with a slowly changing dimension (SCD) table that stores product details. The SCD table is stored in Azure Data Lake Storage Gen2 as Delta format and is updated every few hours. The joined results must be written to a Delta table for near-real-time dashboards. The key requirement is to ensure that the join always uses the latest version of the SCD data without reprocessing the entire stream. The solution must minimize latency and cost. Which approach should you recommend?

Accepted Answer

Use Spark Structured Streaming with a static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot.. Option B is correct because it uses a static DataFrame for the SCD table and refreshes it periodically using a trigger (e.g., a time-based or file-based trigger). This approach ensures that the join always uses the latest version of the SCD data without reprocessing the entire stream, as the static DataFrame is re-read only when the SCD is updated. It minimizes latency and cost by avoiding the overhead of reading the SCD in every micro-batch (as in Option A) or using a streaming join (as in Option D), which is not suitable for batch-updated SCD tables.

Answer

Use Spark Structured Streaming with foreachBatch and read the SCD table inside the foreachBatch function.

Answer

Use Spark Structured Streaming with a batch read of the SCD table in each micro-batch using spark.read.

Answer

Use Spark Structured Streaming with a streaming join on the SCD table by converting it to a stream using readStream.

Question 5

You are a data engineer for a large e-commerce company. The company uses Azure Data Lake Storage Gen2 (ADLS Gen2) as its data lake. A team of data scientists needs to process a massive dataset (approximately 5 TB) stored in Parquet format in the data lake. The dataset contains sales transactions from the past 10 years. The data scientists run a Spark job daily using Azure Synapse Analytics (serverless Spark pool) to compute aggregated sales metrics by product category and region. The job reads the entire dataset each day, performs transformations, and writes the aggregated results back to the data lake. Over the past few weeks, the job has been taking longer to complete, and the data scientists have reported that the job now takes over 6 hours, exceeding the acceptable SLA of 4 hours. They suspect the issue is related to data skew or suboptimal partitioning. You need to optimize the job to reduce execution time. Which approach should you take?

Accepted Answer

Implement incremental processing using Auto Loader with 'directoryListing' mode to process only new files since the last run.. Option C is correct because the job reads the entire 5 TB dataset daily, which is inefficient when only new data needs processing. Auto Loader with 'directoryListing' mode incrementally identifies and processes only new files since the last run, drastically reducing the data volume and execution time. This directly addresses the root cause of the SLA breach—reading unchanged historical data repeatedly—rather than tuning resources or partitioning.

Answer

Increase the executor memory and cores in the Spark pool configuration to handle larger shuffles.

Answer

Repartition the data on the 'product_category' column with a higher number of partitions (e.g., 2000).

Answer

Use a broadcast join hint on the fact table to reduce shuffle operations.

Question 6

You have an Azure Data Factory (ADF) pipeline that runs hourly to ingest data from an on-premises SQL Server into Azure Data Lake Storage Gen2. The pipeline includes a Copy activity that transfers all rows from a source table 'Sales' (approximately 10 million rows) to a Parquet file in the data lake. Recently, you notice that the pipeline runtime has increased from 15 minutes to over an hour. The source database CPU utilization is normal, and the network bandwidth is not saturated. You check ADF monitoring and see high 'Data integration unit' consumption and frequent 'BlobWrite' throttling errors. The storage account is in the same region as the ADF. You need to reduce the pipeline runtime. What should you do?

Accepted Answer

Modify the pipeline to use incremental loads instead of full loads each time.. The pipeline runtime has increased due to frequent BlobWrite throttling errors, indicating that the storage account is hitting its write request limits. By modifying the pipeline to use incremental loads instead of full loads each hour, you reduce the volume of data written per execution, which lowers the number of write operations and avoids throttling. This directly addresses the root cause without requiring a storage tier upgrade or a complete architectural change.

Answer

Change the storage account to Premium tier to increase throughput limits.

Answer

Replace the Copy activity with an Azure Databricks notebook to process the data.

Answer

Use PolyBase in the Copy activity to load data directly into Azure Synapse Analytics.

Question 7

You are a data engineer at a retail company. You have designed a near real-time data processing solution using Azure Stream Analytics. The input is from Azure Event Hubs, which receives clickstream events from the company's e-commerce website. The output is written to an Azure SQL Database table for reporting. Each event includes fields: UserId, ProductId, EventType (e.g., 'click', 'purchase'), and Timestamp. The requirement is to calculate the number of purchases per product in a 5-minute tumbling window and update a SQL table. The Stream Analytics job has been running for a week, but the reporting team notices that the purchase counts in SQL are consistently lower than expected compared to a direct count from Event Hubs. You suspect that late-arriving events are being dropped. The job's configuration includes a 5-minute tumbling window with no late arrival policy. What should you do to fix the issue without losing data?

Accepted Answer

Modify the query to use a larger tumbling window (e.g., 10 minutes) and add a late arrival policy with a 5-minute grace period to allow late events to be included.. Option A is correct because the current 5-minute tumbling window has no late arrival policy, so any event that arrives after the window ends is dropped. By increasing the window size to 10 minutes and adding a 5-minute late arrival grace period, you allow events that arrive up to 5 minutes late to still be included in the correct window, matching the actual purchase count from Event Hubs.

Answer

Modify the query to use TIMESTAMP BY on the EventHubs enqueued time instead of the event's Timestamp field.

Answer

Change the tumbling window to a hopping window with a 1-minute hop size to increase the frequency of output updates.

Answer

Add a second Stream Analytics job to process late-arriving events separately and union the results.

Question 8

You are a data engineer for a financial services company. You manage an Azure Data Lake Storage Gen2 account that stores real-time stock trade data ingested from Azure Event Hubs via Azure Stream Analytics. The data is partitioned by date and symbol. Each day, a downstream Azure Databricks job runs an ETL process to aggregate trades into 5-minute bars and writes the results to a separate container. The Databricks job runs on a cluster with 10 worker nodes (Standard_DS3_v2) using Auto-Scaling enabled (2-10 workers). Recently, the job has been taking longer than expected, and you observe that the cluster is often at 10 workers but still the job duration increased by 30%. The storage account shows high transaction costs. You suspect the issue is related to how data is read. What should you do to optimize the job's performance and reduce costs?

Accepted Answer

Modify the Stream Analytics job to output larger files (e.g., set the minimum file size to 100 MB) and use coalesce in Databricks to reduce the number of output partitions.. Option C is correct because the performance issue stems from reading many small files (small file problem) in Azure Data Lake Storage Gen2, which increases transaction costs and slows down Spark jobs. By configuring Stream Analytics to output larger files (e.g., minimum 100 MB) and using coalesce in Databricks to reduce output partitions, you minimize the number of files read/written, reducing overhead and transaction costs. This directly addresses the root cause—high transaction costs and cluster saturation at 10 workers—without unnecessary scaling or tier changes.

Answer

Convert the data to Avro format to reduce file size.

Answer

Increase the maximum number of workers to 20 and use a larger instance type.

Answer

Move the data to Azure Blob Storage Premium tier to reduce latency.

Question 9

Match the Azure service to its primary data processing use case. Drag each service on the left to the correct use case on the right.

Services: Azure Databricks, Azure Stream Analytics, Azure Data Factory, Azure Synapse Analytics
Use Cases: 
- Real-time event processing
- Orchestration of ETL pipelines
- Big data analytics with Spark
- Enterprise data warehousing

Answer

Azure Databricks - Big data analytics with Spark

Answer

Azure Stream Analytics - Real-time event processing

Answer

Azure Data Factory - Orchestration of ETL pipelines

Answer

Azure Synapse Analytics - Enterprise data warehousing

Question 10

Your company runs a critical data pipeline using Azure Data Factory (ADF) that ingests data from multiple sources into an Azure Synapse Analytics dedicated SQL pool. Recently, you have observed that the pipeline frequently fails with the error: 'Operation for target table failed: 'Cannot insert duplicate key row in object 'dbo.FactSales' with unique index 'PK_FactSales'. The duplicate key value is (20241001, 12345).'' The pipeline uses a Copy activity with a stored procedure sink that merges data into the fact table. The fact table has a clustered columnstore index and a unique constraint on (DateKey, ProductKey). You need to modify the pipeline to handle duplicates without losing data and without impacting performance significantly. What should you do?

Accepted Answer

Configure the Copy activity sink to use 'upsert' behavior with the unique key columns.. Option A is correct because Azure Data Factory's Copy activity supports native upsert behavior when using a stored procedure sink, allowing it to handle duplicate key violations by updating existing rows instead of failing. By specifying the unique key columns (DateKey, ProductKey) in the upsert configuration, the pipeline can merge incoming data into the fact table without requiring manual staging or pre-cleanup, minimizing performance impact by leveraging the existing clustered columnstore index and unique constraint.

Answer

Change the distribution of the fact table to round-robin and remove the unique constraint.

Answer

Use a staging table and then execute a T-SQL MERGE statement to update or insert.

Answer

Add a pre-copy script to delete existing rows that match the incoming data before the copy.

Question 11

A healthcare company stores patient records in Azure Blob Storage. The compliance team requires that all data be encrypted at rest using customer-managed keys (CMK) stored in Azure Key Vault. Additionally, the storage account must be accessible only from a specific virtual network (VNet) and must support versioning to protect against accidental deletion. The storage account is currently using Microsoft-managed keys and has public network access enabled. You need to implement the required changes with minimal downtime. Which course of action should you take?

Accepted Answer

Create a new storage account in the same region with infrastructure encryption enabled and customer-managed keys configured. Enable versioning and VNet access. Use AzCopy to copy data from the old account to the new one. Update applications to use the new storage account. Delete the old account.. Option A is correct because creating a new storage account with infrastructure encryption and customer-managed keys (CMK) is the only way to enable infrastructure encryption, which cannot be enabled on an existing account. Using AzCopy ensures minimal downtime by copying data in the background while applications continue to use the old account until the cutover. Enabling versioning and VNet access on the new account meets all compliance requirements without disrupting the existing environment.

Answer

Modify the existing storage account to use customer-managed keys by updating the encryption settings in the Azure portal. Then, enable versioning and configure firewall rules to allow access from the VNet.

Answer

Delete the existing storage account and recreate it with the same name, enabling customer-managed keys, versioning, and VNet access. Restore data from backups.

Answer

Create a new storage account with the same name in a different region. Enable infrastructure encryption and customer-managed keys. Use Azure Data Factory to copy data from the old account to the new one. Then, delete the old account and update applications to point to the new account.

Question 12

You are tasked with designing a data storage solution for a social media analytics company. They need to store user profile data (JSON) and social media posts (text and images). The data is used for machine learning models that require fast random access to individual user profiles and the ability to run analytical queries over posts. The solution must provide low-latency reads for user profiles (milliseconds) and support for large-scale analytics on posts. Which combination of Azure data services should you recommend?

Accepted Answer

Azure Cosmos DB for user profiles and Azure Data Lake Storage Gen2 for posts. Azure Cosmos DB provides low-latency (millisecond) reads for user profiles via its indexing and partitioning capabilities, ideal for fast random access. Azure Data Lake Storage Gen2 (ADLS Gen2) combines a hierarchical namespace with Blob Storage, enabling large-scale analytical queries on posts using tools like Azure Synapse Analytics or Spark, while efficiently storing text and images.

Answer

Azure Cosmos DB for both user profiles and posts

Answer

Azure SQL Database for both user profiles and posts

Answer

Azure Table Storage for user profiles and Azure Blob Storage for posts

Question 13

Your organization is implementing a data lake using Azure Data Lake Storage Gen2. You have a folder structure like '/data/landing/' for raw data and '/data/curated/' for cleaned data. The data is ingested daily from various sources. You need to ensure that data in the curated zone is immutable and cannot be modified or deleted by anyone, including administrators, for compliance reasons. However, data in the landing zone should be modifiable. What should you do?

Accepted Answer

Enable immutable storage with a time-based retention policy on the curated zone container. Option A is correct because Azure Data Lake Storage Gen2 supports immutable storage at the container level, which enforces a time-based retention policy that prevents any data from being modified or deleted—even by administrators—until the retention period expires. This directly meets the compliance requirement for the curated zone, while leaving the landing zone container unaffected and modifiable.

Answer

Remove the 'Delete' permission from the storage account key

Answer

Set ACLs on the curated zone folder to deny write and delete for all users

Answer

Use Azure RBAC to deny delete and write permissions for all users on the curated zone folder

Question 14

You are a data engineer at a financial services company. You are developing a data processing pipeline that uses Azure Data Factory to copy transactional data from an Azure SQL Database to Azure Data Lake Storage Gen2. The pipeline runs daily and processes about 10 GB of data. You need to implement error handling for the pipeline. Specifically, if the copy activity fails due to a transient error, the pipeline should retry automatically. If the retry fails, the pipeline should log the error and send an email alert to the operations team. What should you do?

Accepted Answer

Configure the copy activity with retry policy (retry count = 2, retry interval = 30 seconds). Add a failure path to a web activity that calls an Azure Logic App to send an email.. Option A is correct because Azure Data Factory allows you to set retry count and retry interval on the activity, and you can add a web activity to send an email via Azure Logic Apps after failure. Option B is wrong because the pipeline-level retry would not allow activity-specific settings. Option C is wrong because Azure Monitor alerts would be reactive, not integrated into the pipeline. Option D is wrong because Azure Functions would require additional coding and setup.

Answer

Use Azure Functions to implement custom retry logic and send email.

Answer

Create an Azure Monitor alert for failed pipeline runs and configure an action group to send an email.

Answer

Set the pipeline retry to 2 and add a storage event trigger on the error file.

Question 15

You are designing a data processing pipeline using Azure Data Factory. The pipeline must ingest data from an HTTP endpoint that returns a JSON array. The data must be transformed by flattening nested arrays and then loaded into an Azure SQL Database table. The pipeline should be triggered daily. You need to choose the appropriate activities and transformations. The solution must be cost-effective and easy to maintain. Which combination of activities should you use?

Accepted Answer

Use a Copy activity to ingest data from the HTTP source into Azure Blob Storage, then a Data Flow activity with a Flatten transformation to flatten the JSON, and finally a Copy activity to load into SQL Database.. Option A is correct. A Copy activity can ingest the JSON from HTTP, and a Data Flow with Flatten transformation can flatten the nested arrays. Option B is wrong because Execute Pipeline is unnecessary. Option C is wrong because a Lookup activity is for reading a single row, not for data ingestion. Option D is wrong because multiple Copy activities are not needed.

Answer

Use a Lookup activity to read the JSON, then a ForEach activity to iterate and insert rows into SQL Database.

Answer

Use a Data Flow activity directly from HTTP source with a Flatten transformation and sink to SQL Database.

Answer

Use two Copy activities: one to copy JSON to Blob Storage, and another to copy from Blob Storage to SQL Database without transformation.

Question 16

You are designing a data lakehouse architecture in Azure using Delta Lake. The solution needs to process batch and streaming data from multiple sources, including IoT devices and CRM systems. You need to ensure data quality by enforcing schema validation and handling schema evolution. You also need to provide a unified catalog for querying. Which service should you use?

Accepted Answer

Azure Databricks Unity Catalog. Option C is correct because Azure Databricks Unity Catalog provides a unified governance solution for data and AI, including schema enforcement and evolution for Delta Lake. Option A is wrong because Azure Purview is for data discovery and lineage, not for schema enforcement. Option B is wrong because Azure Synapse Analytics is a query engine but does not provide the same schema management features as Unity Catalog. Option D is wrong because Azure Data Lake Storage is storage only.

Answer

Azure Purview

Answer

Azure Data Lake Storage Gen2

Answer

Azure Synapse Analytics serverless SQL pool

Question 17

You are a data engineer at a retail company. You need to design a batch processing solution that ingests daily sales data from multiple stores. Each store uploads a CSV file to a dedicated folder in Azure Data Lake Storage Gen2. The files have the same schema but may have minor variations in column order and include null values. After ingestion, you must clean the data by removing rows with null values in the 'SalesAmount' column, convert the 'Date' column from string to date type, and aggregate sales by product category. The output should be stored as Parquet files partitioned by year and month in the same Data Lake. You need to choose a compute service and implement the transformation with minimal coding effort. The solution must be cost-effective and require no cluster management. What should you do?

Accepted Answer

Use Azure Data Factory with a Mapping Data Flow. Configure the source to read all CSV files from the ADLS Gen2 folder. Add a Filter transformation to remove null SalesAmount, a Derived Column to parse the Date, and an Aggregate to sum sales by category. Sink to ADLS Gen2 as Parquet with partition by year and month.. Azure Data Factory Mapping Data Flows provide a code-free visual interface to perform transformations like filter, derived column, and aggregate. It can read from ADLS Gen2, handle schema drift, and write partitioned Parquet files. Option A is correct. Option B requires coding in PySpark and cluster management. Option C uses serverless SQL which is not ideal for complex transformations and file partitioning. Option D uses SQL pool which requires provisioning and is more expensive.

Answer

Use Azure Synapse Serverless SQL pool. Create external tables over the CSV files, write a T-SQL query to filter, cast, aggregate, and use CETAS to write Parquet partitions.

Answer

Use Azure Synapse Dedicated SQL pool. Load CSV files via PolyBase, transform with T-SQL, and use CREATE TABLE AS SELECT to output partitioned Parquet.

Answer

Use Azure Databricks with a PySpark notebook. Mount the ADLS Gen2 storage, read CSV files with schema inference, filter, cast, aggregate, and write partitioned Parquet.

Question 18

You are a data engineer for a healthcare company that processes patient data. You have an Azure Databricks workspace with a cluster configured for data processing. You need to implement a solution that processes streaming data from Azure Event Hubs, enriches it with reference data stored in Azure Cosmos DB, and writes the output to Delta Lake in Azure Data Lake Storage Gen2. The solution must ensure that the data processing is fault-tolerant and can handle schema evolution. The reference data is updated infrequently. You need to choose an approach that minimizes complexity and cost. What should you do?

Accepted Answer

Use Azure Databricks Structured Streaming to read from Event Hubs, use a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table.. Option B is correct because Auto Loader can incrementally process streaming data from Event Hubs (via Event Hubs connector), and it can automatically handle schema evolution. Broadening the reference data join to use a streaming static join in Structured Streaming is efficient for infrequently updated reference data. Option A is wrong because using Change Data Capture adds complexity. Option C is wrong because using Azure Stream Analytics would require additional service cost and integration. Option D is wrong because using batch processing for streaming data would increase latency.

Answer

Use Azure Databricks Auto Loader with Delta Live Tables to ingest streaming data, and use Change Data Capture from Cosmos DB to update the reference data inline.

Answer

Use Azure Data Factory to copy data from Event Hubs to Azure Data Lake Storage Gen2 in batches, then use Azure Databricks to process and enrich with Cosmos DB.

Answer

Use Azure Stream Analytics to ingest from Event Hubs, join with Cosmos DB reference data, and output to Azure Data Lake Storage Gen2 in Parquet format.

Question 19

You are designing a data processing solution for a media company that uses Azure Synapse Analytics. The solution must process video metadata stored in Azure Cosmos DB and combine it with user interaction data from Azure Data Lake Storage Gen2. The combined data must be stored in a dedicated SQL pool for reporting. The data volume is moderate, and the processing should be done using T-SQL. Which approach should you use?

Accepted Answer

Use the serverless SQL pool to create external tables on Cosmos DB and ADLS Gen2, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write the combined data to a table in the dedicated SQL pool.. Option A is correct because Azure Synapse Analytics serverless SQL pool can query Cosmos DB via Azure Cosmos DB analytical store and ADLS Gen2 via OPENROWSET, and then you can use CETAS to write results to the dedicated SQL pool. Option B is wrong because Azure Data Factory would require copy activities and may not be as efficient. Option C is wrong because Azure Databricks uses Spark, not T-SQL. Option D is wrong because PolyBase cannot directly query Cosmos DB.

Answer

Use Azure Data Factory with Copy activities to load data from Cosmos DB and ADLS Gen2 into the dedicated SQL pool, then use a stored procedure to merge.

Answer

Use Azure Databricks to read from Cosmos DB and ADLS Gen2, transform with Spark SQL, and write to the dedicated SQL pool using JDBC.

Answer

Use PolyBase to create external tables on Cosmos DB and ADLS Gen2, then use INSERT...SELECT to load into dedicated SQL pool.

Question 20

Your company is building a real-time dashboard for monitoring website traffic. The data is ingested from web servers into Azure Event Hubs. You need to design a stream processing solution using Azure Stream Analytics that computes the number of unique visitors per minute, per country, and outputs the results to Azure Synapse Analytics for reporting. The solution must handle out-of-order events with a maximum late arrival of 10 seconds. You also need to ensure exactly-once semantics for the output. Which combination of settings should you use?

Accepted Answer

Use a Tumbling window of size 1 minute, set late arrival policy to 10 seconds, and configure output to Azure Synapse Analytics with `Exactly once` semantics.. Option D is correct because a Tumbling window counts events per minute, and the late arrival policy with a 10-second window handles out-of-order events. Exactly-once semantics require that output format is Delta Lake or similar with Idempotent writes; however, Azure Synapse Analytics supports exactly-once with appropriate configuration. Option A is wrong because Hopping windows are for overlapping windows. Option B is wrong because Sliding windows are for point-in-time counts. Option C is wrong because Session windows are for periods of inactivity.

Answer

Use a Sliding window of length 1 minute, set out-of-order policy to 10 seconds, and use 'At least once' output mode.

Answer

Use a Session window with timeout of 1 minute and maximum duration of 10 seconds, set late arrival to 10 seconds, and use 'Exactly once' output mode.

Answer

Use a Hopping window of size 1 minute and hop 1 minute, set late arrival policy to 10 seconds, and use 'Exactly Once' output mode.