DP-900 Practice Test 23 — 15 Questions

Question 1

An e-commerce company uses Azure SQL Database for order processing. The Orders table has columns: OrderID (unique, clustered index), CustomerID, OrderDate, Status, TotalAmount. A common query filters on CustomerID and OrderDate, and sorts by OrderDate descending. The query also returns TotalAmount. Which indexing strategy will produce the best query performance?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create a nonclustered index on (CustomerID, OrderDate DESC) INCLUDE (TotalAmount). It creates a covering index that supports both the equality filter on CustomerID and the range/sort on OrderDate DESC. By including TotalAmount as an included column, the query can be satisfied entirely from the nonclustered index without key lookups to the clustered index, minimizing I/O and improving performance.

Answer

Create a nonclustered index on (OrderDate) INCLUDE (CustomerID, TotalAmount)

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Create a nonclustered index on (OrderDate DESC) INCLUDE (CustomerID, TotalAmount)

Answer

Create a nonclustered index on (CustomerID) INCLUDE (OrderDate, TotalAmount)

Question 2

A healthcare organization stores medical imaging files (DICOM) that are actively used by radiologists for the first 30 days. After 30 days, the files are accessed infrequently for up to 5 years. After 5 years, they must be retained for legal compliance but are accessed very rarely. The organization wants to minimize storage costs. Which strategy should they use to manage the data lifecycle in Azure Blob Storage?

Accepted Answer

Store files in the Hot tier, move to Cool tier after 30 days, then to Archive tier after 5 years.. It aligns the data lifecycle with the access patterns: Hot tier for frequent initial access, Cool tier for infrequent access after 30 days, and Archive tier for long-term compliance after 5 years. Azure Blob Storage lifecycle management policies can automate these transitions, minimizing costs by using the most cost-effective tier for each phase.

Answer

Store all files in the Hot tier and use lifecycle management to move to the Archive tier after 5 years.

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Store all files in the Archive tier from the beginning to minimize cost.

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Store all files in the Cool tier to balance cost and access.

Question 3

A retail company runs analytical reporting queries on a large Sales table in Azure SQL Database. The table contains over 100 million rows and is updated daily with new transactions. The queries aggregate data by product and month, scanning millions of rows per query. The company wants to significantly reduce query execution time without changing the queries. Which indexing strategy should they implement?

Accepted Answer

Create a clustered columnstore index on the table.. A clustered columnstore index is ideal for large data warehousing and analytical workloads because it stores data column-wise, enabling high compression and batch-mode processing. For queries that aggregate millions of rows by product and month, columnstore indexes dramatically reduce I/O and CPU by scanning only the necessary columns and using segment elimination, which directly addresses the requirement to reduce query execution time without changing the queries.

Answer

Create a nonclustered index on the ProductID column.

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Create a filtered index for the most recent month's data.

Answer

Create a clustered rowstore index (default) and rely on database compression.

Question 4

A logistics company tracks shipments. For each shipment, metadata (ID, weight, destination) is stored in a relational table. The route history is a sequence of events (timestamp, location, status) that is frequently appended but never updated or deleted. The application needs to quickly retrieve the latest status of a shipment and occasionally run analytical queries over the full route history. The company wants to minimize storage cost and use Azure services. Which Azure data store should they choose for the route history?

Accepted Answer

Azure Blob Storage with append blobs. Azure Blob Storage with append blobs is the correct choice because route history is write-once, read-many (WORM) data that is frequently appended but never modified or deleted. Append blobs are optimized for sequential append operations, offering low-cost storage for large volumes of event data, and they support fast retrieval of the latest status by reading the last block. This minimizes storage cost while allowing occasional analytical queries over the full history via Azure Synapse or other analytics services.

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Azure Cosmos DB Core (SQL) API

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Azure Table Storage

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Azure SQL Database with a JSON column

Question 5

A company has a legacy application that requires SMB (Server Message Block) file shares to store and access configuration files. They want to migrate this data to Azure without modifying the application. Which Azure storage solution should they use?

Accepted Answer

Azure Files. Azure Files provides fully managed SMB (Server Message Block) file shares in the cloud, supporting the SMB 3.0 protocol. This allows the legacy application to access configuration files over the network using standard file share paths without any code changes, making it the ideal migration target for lift-and-shift scenarios.

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Azure Blob Storage

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Azure Queue Storage

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Azure Disk Storage

Question 6

A global e-commerce platform uses a combination of relational and NoSQL databases. The order management system requires ACID transactions across multiple tables (Orders, OrderItems, Inventory). The product catalog uses a flexible schema to accommodate varying product attributes and is read-heavy. The session store requires low-latency key-value lookups with eventual consistency. Which of the following pairings of data stores best matches these requirements?

Accepted Answer

Order management: Azure SQL Database - Product catalog: Azure Cosmos DB (NoSQL API) - Session store: Azure Cache for Redis. Azure SQL Database provides full ACID transaction support across multiple tables, making it ideal for order management. Azure Cosmos DB (NoSQL API) offers a flexible schema and high read throughput for the product catalog. Azure Cache for Redis delivers sub-millisecond key-value lookups with eventual consistency, perfect for session storage.

Answer

Order management: Azure Cosmos DB (NoSQL API) - Product catalog: Azure SQL Database - Session store: Azure Table Storage

Answer

Order management: Azure Table Storage - Product catalog: Azure SQL Database - Session store: Azure Cosmos DB (NoSQL API)

Answer

Order management: Azure Cosmos DB (Table API) - Product catalog: Azure Cache for Redis - Session store: Azure SQL Database

Question 7

A financial services company uses a dedicated SQL pool in Azure Synapse Analytics to run large-scale analytical queries. During peak hours, complex aggregations consume excessive resources, causing slower performance for other users. The company needs to ensure that critical scheduled management reports always receive guaranteed resources and complete within a predictable timeframe, while less important ad-hoc queries do not interfere. Which feature should they implement to manage query resource allocation?

Accepted Answer

Workload management. Workload management in Azure Synapse Analytics allows you to classify, assign, and govern resources for queries by using workload groups and importance levels. By configuring workload groups, you can guarantee resources for critical scheduled management reports (e.g., assigning high importance) while limiting or deprioritizing less important ad-hoc queries, ensuring predictable completion times during peak hours.

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Result set caching

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Columnstore indexes

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Table distribution

Question 8

A company uses Azure SQL Database to store a large table of sales transactions with columns: TransactionID (primary key), CustomerID, ProductID, SaleDate, Amount. Queries frequently filter by both CustomerID and SaleDate to retrieve sales for a specific customer over a date range. Which indexing strategy will most improve query performance?

Accepted Answer

Create a nonclustered index on (CustomerID, SaleDate). Creating a nonclustered index on (CustomerID, SaleDate) as a composite index directly supports the query predicate that filters by both CustomerID and SaleDate. The index is ordered by CustomerID first, enabling efficient seeks for a specific customer, and then by SaleDate within each customer, allowing the query engine to perform a range scan for the date range without scanning the entire table or sorting. This index is a covering index for this query, as it contains all columns needed for the filter, avoiding key lookups.

Answer

Create a clustered index on SaleDate

Answer

Create a nonclustered index on CustomerID and include SaleDate

Answer

Create a nonclustered index on (SaleDate, CustomerID)

Question 9

A retail company uses Azure SQL Database for an order management system. The Orders table has columns: OrderID (primary key, clustered), CustomerID, OrderDate, TotalAmount. Queries frequently filter on CustomerID and OrderDate, and sort results by OrderDate in descending order. The queries also return the TotalAmount. Which indexing strategy will most improve query performance for these operations?

Accepted Answer

Create a nonclustered index on (CustomerID, OrderDate DESC) INCLUDE (TotalAmount).. It creates a covering nonclustered index that supports both the filter predicates (CustomerID and OrderDate) and the sort order (OrderDate DESC) while including TotalAmount as an included column to avoid key lookups. This index allows SQL Server to satisfy the query entirely from the index pages, minimizing I/O and improving performance.

Answer

Maintain the existing clustered index on OrderID only.

Answer

Create a nonclustered index on (OrderDate DESC) INCLUDE (CustomerID, TotalAmount).

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Create a clustered columnstore index on the entire table.

Question 10

A data file contains records for customer orders. Each record has fields for OrderID, CustomerID, and OrderDate that are present in every record. However, some records include an optional 'DiscountCode' field, and others include an optional 'GiftMessage' field. The file is stored in JSON format. Which type of data does this file represent?

Accepted Answer

Semi-structured data. The JSON file contains records with a fixed set of fields (OrderID, CustomerID, OrderDate) that are always present, but also includes optional fields (DiscountCode, GiftMessage) that may appear in some records but not others. This mix of a consistent schema with flexible, self-describing fields is the hallmark of semi-structured data. JSON itself is a semi-structured format because it uses key-value pairs and allows nested or optional attributes without requiring a rigid schema.

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Structured data

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Unstructured data

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Transactional data

Question 11

A global e-commerce company uses Azure SQL Database for its product catalog. The application experiences high read traffic for product detail pages, often running the same queries for popular items. The database’s write workload is moderate. The company wants to improve read performance without increasing the cost of the primary database tier and without changing the application code. Which Azure SQL Database feature should they implement?

Accepted Answer

Read scale-out. Read scale-out (A) is correct because it offloads read-only queries to a secondary replica of the Azure SQL Database without changing the application code. By setting `ApplicationIntent=ReadOnly` in the connection string, the database routes read queries to a read-only replica, improving performance for high-read workloads like product detail pages while keeping the primary tier unchanged and avoiding additional cost for a higher tier.

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Active geo-replication

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Azure SQL Database elastic pool

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Query Performance Insight

Question 12

A financial services company processes real-time stock trade data from multiple exchanges. Trades are ingested into Azure Event Hubs. The company needs to compute a 5-minute sliding window average of trade prices per stock symbol and ensure that each trade is processed exactly once within the window. The aggregated results must be stored in Azure SQL Database for historical reporting and also sent to a Power BI dashboard for near real-time visualization. Which Azure service should be used for the real-time processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is purpose-built for real-time stream processing with native support for time-based windowing (e.g., 5-minute sliding window) and exactly-once semantics when used with Azure Event Hubs as input and Azure SQL Database as output. It can directly compute the sliding window average of trade prices per stock symbol and output results to both Azure SQL Database for historical storage and Power BI for near real-time visualization, all without requiring additional code or infrastructure management.

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Azure Databricks with Structured Streaming

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Azure Data Factory

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Azure Event Hubs

Question 13

A financial services company uses Azure Synapse Analytics to process large volumes of transaction data. They have a dedicated SQL pool (formerly SQL DW) that ingests curated, aggregated data nightly from a data lake. Data analysts need to run ad-hoc, exploratory T-SQL queries on raw transaction data stored as Parquet files in Azure Data Lake Storage Gen2. These queries vary widely in complexity and frequency. The company wants to minimize costs for these ad-hoc queries while still using full T-SQL capabilities. Which approach should they recommend?

Accepted Answer

Create a serverless SQL pool endpoint to query the data lake directly.. Serverless SQL pool in Azure Synapse Analytics is designed for ad-hoc, on-demand querying of data lake files (like Parquet) without provisioning or paying for dedicated compute resources. It supports full T-SQL syntax and charges only for the data processed per query, making it cost-effective for exploratory workloads with variable complexity and frequency.

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Use external tables in the dedicated SQL pool to query the data lake directly.

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Load the raw data into the dedicated SQL pool before querying.

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Use Azure Data Explorer to query the data lake.

Question 14

A company needs to store archived log files that are rarely accessed but must be retained for regulatory compliance. The logs are text-based and each file is about 10 MB. They want the lowest storage cost while ensuring the data is durable and can be read when needed. Which Azure Blob Storage access tier should they choose?

Accepted Answer

Archive. The Archive tier is the correct choice because it offers the lowest storage cost for data that is rarely accessed and must be retained for long periods. Archived log files that are text-based and 10 MB each fit this profile perfectly, as the Archive tier is designed for data that can tolerate a retrieval latency of several hours (up to 15 hours for standard priority) while providing the same high durability (99.9999999999% or 11 nines) as other tiers. The data remains fully durable and can be read when needed by first rehydrating it to an online tier (Hot, Cool, or Cold) before access.

Answer

Hot

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Cool

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Cold

Question 15

A database administrator manages an Azure SQL Database with a table that has a clustered index on OrderID. Frequent queries filter on OrderDate and then sort the results by CustomerID. These queries perform poorly. Which indexing strategy will most improve performance for these specific queries?

Accepted Answer

Create a nonclustered index on (OrderDate, CustomerID). The query filters on OrderDate and then sorts by CustomerID. A nonclustered index on (OrderDate, CustomerID) supports both the WHERE clause (by OrderDate) and the ORDER BY clause (by CustomerID) as a covering index, allowing the database engine to perform a single index seek or scan without needing a separate sort operation. This directly addresses the performance bottleneck by eliminating the need to sort the results after filtering.

Answer

Create a nonclustered index on (CustomerID, OrderDate)

Answer

Change the clustered index to (OrderDate)

Answer

Create a filtered index on OrderDate WHERE CustomerID IS NOT NULL