Microsoft DP-203 Exam Dumps & Practice Test Questions
Question No 1:
You are tasked with designing a real-time data processing solution using Azure Stream Analytics to ingest live data from social media streams. The data will be stored in Azure Data Lake Storage for further processing. Two key consumers of this data will be:
Azure Databricks (for advanced analytics and machine learning)
Azure Synapse Analytics using PolyBase (for data warehousing and SQL querying)
Your objective is to choose the most appropriate output format in Azure Stream Analytics that:
Minimizes errors during data querying in Databricks and Synapse PolyBase
Allows fast querying performance
Retains full data type information
Which output format would you recommend to meet these criteria?
A. JSON
B. Parquet
C. CSV
D. Avro
Correct Answer: B. Parquet
Explanation:
When dealing with big data in Azure, selecting the correct output format is crucial for optimizing performance, ensuring compatibility, and maintaining data integrity. Parquet is a columnar format that is especially suited for analytical workloads, as it supports efficient querying, schema preservation, and high compression.
Parquet is a columnar storage format, which organizes data column-by-column instead of row-by-row (as in JSON or CSV). This columnar structure enables Azure Databricks and PolyBase in Synapse Analytics to query specific columns, improving performance by reducing I/O operations and query time. Additionally, Parquet is self-describing, meaning it includes the schema (including data types), ensuring that data consistency is maintained across different platforms and tools, thus minimizing errors when querying.
JSON is flexible and human-readable, but it does not offer efficient querying and does not enforce data types. This can lead to errors and performance issues during data processing in systems like Databricks and Synapse.
CSV is widely used but lacks support for complex schemas or data types. When working with large datasets and analytical tools, the absence of explicit schema definition in CSV files can lead to data type mismatches and parsing issues.
Avro supports schema evolution and is better suited for row-based storage or messaging, rather than analytical queries. It is less efficient than Parquet for complex queries in big data environments.
Therefore, Parquet is the most appropriate format as it ensures optimal performance, schema compatibility, and minimal errors during querying in Databricks and Synapse PolyBase.
Question No 2:
You are managing a dedicated SQL pool in Azure Synapse Analytics, which contains a partitioned fact table called dbo.Sales. This large table has been optimized with partitions to enhance performance and manageability. You also have a staging table named stg.Sales, structured exactly like dbo.Sales, including the same partition definitions.
Your task is to replace the first partition of dbo.Sales with the corresponding partition data from stg.Sales. The solution must be optimized to minimize load times and maximize performance.
What should you do to achieve this efficiently?
A. Insert the data from stg.Sales into dbo.Sales.
B. Switch the first partition from dbo.Sales to stg.Sales.
C. Switch the first partition from stg.Sales to dbo.Sales.
D. Update dbo.Sales from stg.Sales.
Correct Answer: C. Switch the first partition from stg.Sales to dbo.Sales.
Explanation:
In Azure Synapse Analytics, partition switching is an optimal way to replace data in partitioned tables, especially when both tables have the same structure and partitioning scheme. Switching partitions is a metadata operation, which is much faster and more efficient than traditional row-level operations like INSERT or UPDATE.
Partition switching is ideal for efficiently replacing a partition in one table with a partition from another table that shares the same schema and partitioning definition. By using this method, you avoid the need to physically copy or update rows, which can be time-consuming and resource-intensive, especially for large datasets. In this case, switching the first partition from stg.Sales to dbo.Sales will instantly replace the data in the target partition without affecting the rest of the table.
Option A (Insert) and Option D (Update) both involve row-level operations, which are slower and require significant resources when working with large tables. These methods would also lead to more complex transaction management and longer load times.
Option B (Switch the first partition from dbo.Sales to stg.Sales) is incorrect because it suggests removing data from dbo.Sales and placing it into stg.Sales, which is the opposite of the required operation.
Therefore, switching the partition from stg.Sales to dbo.Sales is the most efficient method to achieve this task with minimal load time and resource consumption.
Question No 3:
You are optimizing the partition strategy for a large fact table in a dedicated SQL pool within Azure Synapse Analytics. The table has the following characteristics:
It contains sales data for 20,000 products.
It uses hash distribution on the ProductID column.
It holds 2.4 billion rows spanning the years 2019 and 2020.
The storage uses a clustered columnstore index (CCI).
To achieve optimal performance and compression, what is the ideal number of partition ranges to configure for this table?
A. 40
B. 240
C. 400
D. 2,400
Correct Answer: B. 240
Explanation:
When working with large fact tables in Azure Synapse Analytics, it is crucial to select the correct number of partitions to ensure efficient data processing, optimal compression, and good query performance, especially when using a clustered columnstore index (CCI).
Partitioning is important for controlling how data is distributed and queried. With CCI, the best performance is achieved when each partition contains a manageable number of rows, which facilitates efficient compression and query pruning. Ideally, a partition should contain around 1 million rows for optimal performance.
In this case, with 2.4 billion rows across two years, you would want to divide the data in such a way that each partition contains a reasonable number of rows. If you choose 240 partitions, each partition would contain approximately 10 million rows, which strikes an ideal balance between granularity for query pruning and maintaining large enough partitions to ensure efficient compression and performance.
Option A (40 partitions) would result in partitions containing 60 million rows, which could hinder performance due to large rowgroups, leading to slower query execution and inefficient compression.
Option D (2,400 partitions) would create partitions with 1 million rows each, which could result in excessive overhead from too many partitions, leading to poor compression and inefficient resource utilization.
Therefore, 240 partitions provide the best balance for performance and compression, ensuring optimal query speed and data storage efficiency.
Question No 4:
You are building a data processing pipeline in Azure that includes generating and storing Parquet files through Azure Data Factory (ADF). These files are then saved in Azure Data Lake Storage Gen2. To perform analytics, you plan to query this data using a serverless SQL pool in Azure Synapse Analytics. One of your main objectives is to reduce storage expenses while still ensuring the queries remain efficient.
What should you do to achieve both low storage cost and optimal query performance?
A. Apply Snappy compression while writing the Parquet files
B. Use OPENROWSET in Synapse SQL queries to access Parquet data
C. Create an external table in Synapse with only the necessary columns
D. Store all fields in the Parquet files as string data types
Correct Answer: A
Explanation:
To optimize storage costs while maintaining efficient access to your data, the best strategy is to apply Snappy compression when writing Parquet files. Azure Data Lake Storage Gen2 charges based on the volume of data stored and read, so minimizing file size directly impacts your operational costs.
Parquet is a highly efficient columnar storage format, especially suitable for analytics workloads. Adding Snappy compression improves this efficiency by reducing the size of the files without significantly increasing read latency. Snappy is preferred in analytical environments because it offers fast compression and decompression, even though its compression ratio might be slightly lower than that of algorithms like Gzip. Its speed ensures quick querying, which is critical when using serverless SQL pools for interactive analytics.
Let’s evaluate the other options:
B (OPENROWSET): This is a method for querying external data in serverless SQL pools but does not affect the file size or storage costs. It's useful for access, not for optimization.
C (external table with fewer columns): Reducing columns can speed up queries but doesn’t reduce the physical file size in storage. Parquet files still hold the same data unless transformed prior to loading.
D (store all data as strings): This would increase storage size and reduce performance because strings take more space than native numeric types and require type conversions at query time.
Therefore, applying Snappy compression when writing Parquet files is the most effective approach to achieve both cost reduction and performance efficiency.
Question No 5:
You are designing a data pipeline in Azure Data Factory (ADF) to process daily sales data files stored in an Azure Data Lake Storage Gen2 account. The files arrive in a folder partitioned by date (/sales/2025/05/10/). You want to automatically pick up new files daily and transform them using Azure Synapse Analytics.
What should you use in Azure Data Factory to ensure the pipeline only processes new files as they arrive?
A) Tumbling Window Trigger
B) Schedule Trigger
C) Event-Based Trigger
D) Manual Trigger
Correct Answer: C
Explanation:
This scenario involves triggering a data pipeline automatically when new files arrive in Azure Data Lake Storage Gen2. The goal is to ensure that only new files are processed, and the trigger should be responsive to file arrival.
A) Tumbling Window Trigger:
Tumbling Window Triggers in ADF run at periodic intervals and maintain state between runs, which is useful for managing batches of time-bound data. However, this option isn't truly event-driven—it runs on a schedule and checks for data. It might miss new files if the file is delayed or duplicated in some way outside the window.B) Schedule Trigger:
This trigger also operates on a fixed schedule (e.g., every hour or every day), regardless of whether new data has arrived. While useful for predictable batch processes, it can be inefficient if files don’t arrive exactly on schedule or if there are no new files.C) Event-Based Trigger:
This is the most suitable option for this scenario. Event-Based Triggers in Azure Data Factory listen to events generated by Azure Blob Storage or Data Lake Storage (via Azure Event Grid) and respond when a new blob is created. This ensures that the pipeline automatically kicks off only when a new file arrives, making the process efficient, real-time, and automated without unnecessary checks.D) Manual Trigger:
Manual Triggers require an engineer or administrator to start the pipeline manually, which is inefficient and doesn’t scale well for automated, daily file ingestion scenarios.
Therefore, Event-Based Trigger (C) is the correct answer, as it best meets the requirement of automatically triggering data ingestion only when new files are dropped into the storage account.
Question No 6:
You are assigned the task of developing a data mart for the Human Resources (HR) department within your company. This data mart will be built using Azure Synapse Analytics' dedicated SQL pool and is intended to support reporting and analysis on employee records and financial transaction data (such as salaries and benefit disbursements).
The source system supplies a flat file extract that includes the following fields:
EmployeeID
FirstName
LastName
Recipient
GrossAmount
TransactionID
GovernmentID
NetAmountPaid
TransactionDate
Your objective is to design a star schema that will support efficient reporting and adhere to dimensional modeling principles.
Based on standard dimensional modeling practices, which TWO of the following tables should you create as part of your star schema?
A. A dimension table for Transaction
B. A dimension table for Employee Transaction
C. A dimension table for Employee
D. A fact table for Employee
E. A fact table for Transaction
Correct Answers: C and E
Explanation:
In dimensional modeling, commonly used in data marts and warehouses, tables are divided into two primary categories: dimension tables and fact tables. This structure forms the basis of a star schema, which is designed for analytical querying and reporting.
Dimension tables store descriptive attributes about business entities. These are usually text-based fields that provide context for the data stored in fact tables. Examples include names, categories, or identifiers that can be used to filter or label data in reports.
Fact tables, in contrast, store measurable, numeric data that can be aggregated or analyzed. These tables usually include foreign keys referencing dimensions and contain metrics such as sales, counts, or monetary values.
In this HR scenario:
Fields like EmployeeID, FirstName, LastName, and GovernmentID are descriptive and pertain to the individual employee. These fields should be stored in a dimension table, typically named something like DimEmployee.
Fields such as GrossAmount, NetAmountPaid, and TransactionDate are clearly measurable, transactional data points. These should be stored in a fact table, often named FactTransaction, which may also include TransactionID as a unique identifier and foreign keys pointing to dimensions.
Let's evaluate the options:
A. Transaction as a dimension is incorrect, as transactions represent measurable facts, not descriptive entities.
B. EmployeeTransaction as a dimension is ambiguous and risks combining two separate entities, violating normalization principles.
D. Employee as a fact table is inappropriate, as employees are not measures; they are entities with attributes.
Therefore, the most appropriate star schema includes a dimension table for Employee (C) and a fact table for Transaction (E). This design allows analysts to examine transactional data over time and across various employee characteristics.
Question No 7:
You are designing a dimension table for a data warehouse where it's important to retain a full history of attribute changes over time. Each time an attribute value changes, the table should keep the previous version and insert a new row with the updated data.
Which type of Slowly Changing Dimension (SCD) best fulfills this requirement?
A. Type 0 – Fixed Dimension (No Changes Allowed)
B. Type 1 – Overwrite Old Data
C. Type 2 – Add New Row for Each Change
D. Type 3 – Store Only Limited History in Same Row
Correct Answer: C
Explanation:
Slowly Changing Dimensions (SCDs) are a core concept in dimensional modeling used to manage how attribute values change over time. When building a data warehouse, particularly for analytical and historical reporting, it’s essential to choose the correct SCD type based on the level of historical data retention required.
SCD Type 2 is the ideal solution when full historical tracking is essential. Under this model, any time an attribute in the dimension changes—such as a customer's address or a product's category—a new row is added to the dimension table. The existing row is preserved to reflect the past state, while the new row contains updated values. This allows users to query and analyze the state of the data as it existed at any given point in time.
Typically, Type 2 tables include additional metadata such as surrogate keys, effective start and end dates, and a current record flag. These help in filtering and versioning data accurately across time-based queries, audits, and trend analyses.
Let’s briefly contrast this with the other SCD types:
Type 0 does not allow any updates—data remains fixed forever, suitable for static attributes like birthdates.
Type 1 updates records in-place, meaning old data is lost when changes occur. It's used when historical tracking is not necessary.
Type 3 stores a limited history—usually the current and one previous value—by adding new columns instead of rows. It's unsuitable for scenarios with frequent changes.
Because your requirement is to retain all historical changes and track data evolution comprehensively, SCD Type 2 is the most appropriate and widely used approach in enterprise-grade data warehouses.
Question No 8:
You are planning to load multiple CSV files stored in Azure Data Lake Storage Gen2 into an Azure Synapse Analytics dedicated SQL pool using PolyBase. Each file includes a header row that must be skipped during data ingestion.
What is the correct sequence of steps required to configure the database objects before performing the batch load?
A. Create an external file format with First_Row set
B. Create a database scoped credential using a service principal
C. Create an external data source pointing to the storage account
Correct Order: B → C → A
Explanation:
To successfully use PolyBase for importing data into a dedicated SQL pool in Azure Synapse Analytics, you must first set up a series of database objects in a specific sequence. These configurations allow Synapse to securely connect to your external data (CSV files in Azure Data Lake Storage Gen2) and understand how to interpret the files’ format during the load.
Create a Database Scoped Credential (B):
The first step is to configure a secure connection between Synapse and the data lake. This is done by creating a database scoped credential that uses an Azure Active Directory application and service principal key. This credential is needed to authenticate and authorize Synapse to access the files in your storage account.Create an External Data Source (C):
Once the credential is in place, the next step is to define an external data source. This points to your Azure Data Lake using the ABFS (Azure Blob File System) path format and ties back to the scoped credential for secure access. This object tells Synapse where to find the data files.Create an External File Format with First_Row (A):
Finally, create an external file format that defines how the CSV files should be read. Since each file includes a header, you must set the FIRST_ROW = 2 option. This ensures PolyBase starts reading from the second row, thus skipping the header during ingestion. You can also define other properties such as delimiter type, text qualifier, and compression here.
Executing these steps in the correct sequence ensures accurate and secure data ingestion into your Synapse SQL pool. Failing to configure them properly may lead to authentication errors or incorrectly loaded data.
Question No 9:
You are designing a transaction fact table in Azure Synapse Analytics (dedicated SQL pool) to store data for the first half of the year 2020. Your design goals are to:
Allow fast deletion of records that are older than 10 years.
Reduce I/O during year-to-date (YTD) analytical queries.
Which of the following design choices best satisfies both requirements?
A. Use a Clustered Index, Round Robin distribution, partition by [TransactionAmount], and define a single yearly partition for 2020
B. Use a Clustered Columnstore Index, Hash distribution on [TransactionTypeID], partition by [TransactionDateID], and define monthly partitions (e.g., 20200101, 20200201, ..., 20200601)
C. Use a Nonclustered Index, Replicate distribution, partition by [CustomerID], and create quarterly partitions for 2020
D. Use a Clustered Columnstore Index, Round Robin distribution, no partitioning, and load all data into a single table structure
Correct Answer: B
Explanation:
When building large-scale fact tables in Azure Synapse Analytics, both performance optimization and data lifecycle management must be considered. To address the two main goals in the scenario—fast deletion of old records and efficient YTD querying—option B provides the most appropriate solution.
Using a Clustered Columnstore Index improves performance significantly by storing data in a compressed, columnar format that is optimized for analytical workloads. This design reduces the amount of I/O by scanning only the relevant columns needed in queries, making it ideal for year-to-date and summary reporting.
The Hash distribution on [TransactionTypeID] spreads data evenly across compute nodes, enabling parallel processing and minimizing data movement during queries. This is preferable for large fact tables where uniform distribution helps prevent performance bottlenecks.
Partitioning by [TransactionDateID] ensures that data can be logically segmented by time. This is critical when you need to delete old data. Instead of executing row-level deletions—which are costly—you can simply drop entire partitions, which is much faster and more efficient.
Finally, defining monthly partition ranges (e.g., 20200101 to 20200601) provides granular control over data storage and access. It enables partition pruning, which reduces the amount of data scanned during YTD queries, thereby improving performance. It also simplifies data archival and maintenance tasks.
In contrast:
Option A lacks appropriate indexing and uses an ineffective partition column.
Option C misuses Replicate for a large fact table.
Option D avoids partitioning altogether, which limits flexibility for data retention and query optimization.
Thus, option B is the most balanced and scalable approach for this scenario.
Question No 10:
You are building a real-time analytics solution using Azure Stream Analytics. Your input source is IoT sensor data streaming through Azure Event Hubs. You need to store processed data in a highly scalable, analytical store that allows fast querying using T-SQL.
Which output sink should you choose?
A) Azure Synapse Analytics
B) Azure Cosmos DB
C) Azure SQL Database
D) Azure Blob Storage
Correct Answer: A
Explanation:
This scenario focuses on building a real-time analytics pipeline where incoming data from Azure Event Hubs (via IoT sensors) needs to be processed and stored in a data store that supports scalable analytics and fast querying using T-SQL.
Let’s evaluate each option:
A) Azure Synapse Analytics:
Azure Synapse Analytics is a powerful analytics service that combines enterprise data warehousing and Big Data analytics. It supports MPP (Massively Parallel Processing), enabling fast querying over large datasets, and is fully compatible with T-SQL. It integrates seamlessly with Azure Stream Analytics as an output sink and is purpose-built for large-scale data analytics. This makes it the best choice for storing and querying processed IoT data in near real-time.B) Azure Cosmos DB:
While Cosmos DB is excellent for globally distributed NoSQL data storage with low latency, it is not designed for analytical workloads at scale. It does not support T-SQL natively (it uses its own query language depending on the API used) and is more suited for operational, not analytical, workloads.C) Azure SQL Database:
Azure SQL Database supports T-SQL and can be used as an output sink for Stream Analytics. However, it is a single-node system and does not scale well for very high-throughput or large-scale analytical queries when compared to Synapse. It’s more appropriate for transactional workloads rather than large-scale analytics.D) Azure Blob Storage:
Blob Storage is great for storing raw data or processed results in flat files such as CSV, Avro, or Parquet. While it's useful for archiving and later batch processing, it doesn’t support querying directly with T-SQL unless further processed through Synapse or another engine.
In conclusion, Azure Synapse Analytics (A) is the best fit here because it supports T-SQL, scales well for large volumes of data, and integrates efficiently with Azure Stream Analytics, fulfilling both the performance and query ability requirements.
