dp 203 case study questions Practice Exam 2025: Latest Questions

You need to ingest raw JSON files from Azure Data Lake Storage Gen2 into Azure Synapse Analytics. The files have varying fields over time, and you want to avoid frequent table schema changes while still enabling SQL analytics. What is the best approach?

A team runs a daily Azure Synapse pipeline that copies data from an on-premises SQL Server into Azure Data Lake Storage Gen2 using a self-hosted integration runtime (SHIR). The pipeline fails with connectivity errors after the SHIR host was moved to a different subnet. What should you validate first?

You want to restrict access to specific folders in an Azure Data Lake Storage Gen2 container so that only a particular Azure AD group can read files under /curated/finance. Which authorization mechanism should you use?

You have a Spark job in Azure Synapse that writes Parquet files to a data lake. Query performance in Synapse serverless SQL is slow due to reading many small files. What is the best practice to improve performance?

You need to implement an incremental load from an Azure SQL Database table into a dedicated SQL pool in Azure Synapse. The source table has a reliable LastModifiedDate column. What is the recommended approach in a Synapse pipeline?

A streaming pipeline ingests events from Azure Event Hubs and must ensure that downstream consumers can reprocess events from a specific point in time after an incident. Which approach best supports replayability in a data lake-centric design?

Your organization requires that secrets (such as database passwords and service principal keys) are not stored in Synapse pipeline JSON. You also need centralized rotation and auditing of secret access. What should you use?

A dedicated SQL pool in Azure Synapse shows frequent query failures due to tempdb/resource contention during peak hours. You want to prioritize critical workloads and isolate resources for different teams. What should you implement?

You are designing a lakehouse solution on ADLS Gen2. Multiple Spark jobs concurrently upsert data into a curated table and readers must always see a consistent snapshot without partial updates. Which storage format and approach best meets these requirements?

A Spark structured streaming job in Azure Databricks writes to Azure Synapse dedicated SQL pool. The job occasionally produces duplicate rows after restarts. You need an approach that provides end-to-end exactly-once results in the sink table. What should you do?

You use Azure Data Factory (ADF) to copy data from an on-premises SQL Server to Azure Data Lake Storage Gen2. The on-premises environment can only make outbound HTTPS connections. Which component is required to enable the copy?

You are writing Spark code in Azure Synapse Analytics to read raw files from Azure Data Lake Storage Gen2 using the ABFS driver. Which identifier should you use to address a container in a storage account?

You need to enforce that all connections to an Azure SQL database used by data pipelines require encryption in transit. Which setting best meets the requirement?

You are building an incremental load into a dedicated SQL pool in Azure Synapse Analytics. Source data contains inserts and updates, and you must keep a full history of changes for auditing. Which approach is most appropriate?

You have an Azure Stream Analytics job that reads events from Event Hubs and writes aggregates to Azure Data Lake Storage Gen2. You need to ensure that late-arriving events are included in the correct time window aggregates. What should you configure?

You have a dedicated SQL pool with a large fact table and several small dimension tables. Queries frequently join the fact table to the dimensions on keys. You want to reduce data movement during joins and improve query performance. What should you do for the dimension tables?

Your organization mandates that secrets (connection strings, keys) used by Azure Data Factory pipelines must not be stored in pipeline JSON or parameter files. What is the recommended approach?

You are querying Parquet files in a serverless SQL pool in Azure Synapse Analytics. Queries are slow because they scan many files and columns. You want to optimize performance with minimal changes to the data format. What should you do?

You are using Delta Lake tables in a Lakehouse pattern on Azure Data Lake Storage Gen2. Multiple jobs concurrently write to the same Delta table and occasionally you see write conflicts. You must maintain ACID guarantees and prevent partial updates. What should you rely on?

A data platform uses Azure Synapse dedicated SQL pool with Row-Level Security (RLS) to restrict data by user. Analysts connect through Power BI using an Azure AD security group. Users report they can see all rows, ignoring the RLS predicate. What is the most likely cause?

You need to land raw JSON files from multiple source systems into Azure Data Lake Storage Gen2. The pipeline must ensure that files are never overwritten and that each load is isolated by time to support replay. What is the best approach in Azure Data Factory?

You are designing a dimensional model in a dedicated SQL pool in Azure Synapse Analytics. You need a surrogate key for a large fact table and want fast, scalable inserts without coordinating key generation in the ETL process. Which approach should you use?

A Synapse pipeline Copy activity intermittently fails with an error indicating too many requests (throttling) when reading from Azure Data Lake Storage Gen2. You need to reduce failures with minimal changes to the dataset and pipeline logic. What should you do first?

You are building a lakehouse on ADLS Gen2 using Parquet files. Analysts frequently filter by date range and customerId, and query performance is inconsistent due to excessive file scanning. You want to improve performance primarily through data layout and pruning. What should you implement?

You have a medallion architecture (bronze/silver/gold) in ADLS Gen2. Business users must only see curated gold data, while data engineers need access to bronze and silver. You must enforce these restrictions with centralized governance across Synapse and other Azure analytics engines without managing ACLs on every folder. What should you use?