DP-900 Practice Test 22 — 15 Questions

Question 1

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?

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

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.

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Create a nonclustered index on (CustomerID, OrderDate)

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Change the clustered index to (OrderDate)

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Create a filtered index on OrderDate WHERE CustomerID IS NOT NULL

Question 2

An e-commerce company runs a product inventory database on Azure SQL Database. During a flash sale, write transactions are slow because many read queries are running simultaneously and consuming resources. The company wants to isolate read workloads without modifying application code or database schemas. Which Azure SQL Database feature should they implement?

Accepted Answer

Read scale-out. Read scale-out (B) is correct because it allows read-only queries to be offloaded to a read-only replica of the database, freeing up the primary replica for write transactions. This feature is built into Azure SQL Database at the Business Critical and Premium service tiers, and it requires no application code changes—just a connection string modification to use the `ApplicationIntent=ReadOnly` parameter. It directly addresses the performance bottleneck caused by concurrent read queries during the flash sale.

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Active geo-replication

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Auto-failover groups

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Elastic pools

Question 3

A company has an existing IoT application that uses Apache Cassandra for time-series sensor data. They want to migrate to Azure's fully managed NoSQL database service while continuing to use the Cassandra Query Language (CQL) and benefiting from global distribution and low latency. Which Azure Cosmos DB API should they use?

Accepted Answer

Cassandra API. The Cassandra API for Azure Cosmos DB is wire-protocol-compatible with Apache Cassandra, meaning you can use existing CQL (Cassandra Query Language) tools, drivers, and code with minimal changes. It provides a fully managed, globally distributed NoSQL database with low-latency reads and writes, which directly matches the company's requirement to migrate from self-managed Cassandra while preserving their CQL-based application logic.

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Core (SQL) API

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MongoDB API

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Gremlin API

Question 4

A company is migrating their on-premises data warehouse, which is built on a Netezza appliance, to Azure. The data warehouse contains over 10 terabytes of data and supports complex BI queries with multiple joins and aggregations. The company requires a cloud-based solution that provides massively parallel processing (MPP) to handle large-scale queries efficiently. They also need to integrate with existing ETL tools like Azure Data Factory and provide native connectivity to Power BI. Which Azure service should they choose?

Accepted Answer

Azure Synapse Analytics dedicated SQL pool. Azure Synapse Analytics dedicated SQL pool is the correct choice because it provides massively parallel processing (MPP) architecture designed for petabyte-scale data warehousing, exactly matching the 10+ TB requirement. It natively integrates with Azure Data Factory for ETL and offers built-in Power BI connectivity via the T-SQL endpoint, supporting complex BI queries with multiple joins and aggregations.

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Azure SQL Database

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Azure Databricks

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Azure HDInsight

Question 5

A company uses Azure Synapse Analytics dedicated SQL pool for a large data warehouse. The fact table contains billions of rows and is hash-distributed on ProductID. Frequent queries join this fact table with a small Store dimension table (10,000 rows) and a medium-sized Product dimension table (500,000 rows). The queries aggregate sales by store and product for recent months, but run slowly due to data movement during joins. Which design change will most reduce data movement and improve query performance?

Accepted Answer

Replicate the Store dimension table. Replicating the small Store dimension table (10,000 rows) across all compute nodes eliminates the need to shuffle data during joins with the fact table. In Azure Synapse dedicated SQL pool, replicated tables store a full copy on each distribution, so queries that join a replicated table with a distributed fact table avoid costly data movement, significantly improving performance for frequent aggregation queries.

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Change the distribution of the fact table to round-robin

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Change the distribution key of the fact table to StoreID

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Add a nonclustered index on the StoreID column in the fact table

Question 6

A manufacturing company collects sensor readings from thousands of IoT devices. Each reading consists of a device ID, a timestamp, and a numeric value. The data is stored as key-value pairs and must support low-latency reads and writes at a global scale. The company also needs to query the data by device ID and time range. Which Azure Cosmos DB API should they choose?

Accepted Answer

Table API. The Table API is the correct choice because it is designed for key-value workloads with a schema-less design, supporting low-latency reads and writes at global scale. It allows querying by partition key (device ID) and row key (timestamp) to efficiently retrieve data by device ID and time range, matching the IoT sensor data requirements.

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Core (SQL) API

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MongoDB API

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Gremlin API

Question 7

A company is migrating a 500 GB financial database to Azure. The database requires low read/write latency, supports a high number of concurrent transactions, and must have a Recovery Point Objective (RPO) of less than 5 seconds and a Recovery Time Objective (RTO) of less than 30 minutes. The company is willing to pay more for these guarantees. Which Azure SQL Database service tier should they choose?

Accepted Answer

Business Critical. Business Critical is the correct choice because it provides the lowest read/write latency through always-on secondary replicas and uses local SSD storage, which is essential for high-concurrency transactional workloads. It also guarantees an RPO of less than 5 seconds via synchronous data replication and an RTO of under 30 minutes, meeting the strict recovery requirements.

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General Purpose

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Hyperscale

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Serverless (General Purpose)

Question 8

A retail company collects raw clickstream data from its website as JSON files. Data scientists need to run exploratory analytics on this raw data without a predefined schema. BI analysts also need to generate weekly sales reports from aggregated transactional data stored in a relational format. Which combination of data storage approaches best meets these needs?

Accepted Answer

Store raw data in Azure Data Lake Storage and aggregated data in Azure SQL Database. Azure Data Lake Storage (ADLS) is optimized for storing raw, schema-on-read data like JSON files, enabling data scientists to run exploratory analytics without a predefined schema. Azure SQL Database provides a relational structure with ACID compliance, ideal for BI analysts generating weekly sales reports from aggregated transactional data. This combination directly addresses both unstructured raw data and structured reporting needs.

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Store raw data in Azure Blob Storage and aggregated data in Azure Cosmos DB

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Store raw data in Azure Table Storage and aggregated data in Azure Data Lake Storage

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Store raw data in Azure SQL Database and aggregated data in Azure Blob Storage

Question 9

A logistics company ingests real-time GPS data from delivery vehicles via Azure Event Hubs. The data includes vehicle ID, latitude, longitude, and timestamp. The company also has historical route plan data stored as CSV files in Azure Data Lake Storage Gen2. Data analysts need to combine the live stream with the historical data in near real-time to create a dashboard showing if vehicles are on schedule. They also need to run complex T-SQL queries on the combined dataset for ad-hoc reporting. Which Azure service should they use as the primary analytics platform?

Accepted Answer

C: Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest real-time data from Azure Event Hubs via its built-in streaming capabilities (e.g., using Synapse Pipelines or Spark Structured Streaming) and combine it with historical data stored in Azure Data Lake Storage Gen2. It supports complex T-SQL queries through its dedicated SQL pool (formerly SQL Data Warehouse) for ad-hoc reporting, enabling near real-time dashboards and interactive analytics on the combined dataset.

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A: Azure Stream Analytics

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B: Azure Data Lake Analytics

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D: Azure Analysis Services

Question 10

A hospital uses Azure SQL Database to store patient appointment records. The 'Appointments' table has columns: AppointmentID (primary key), PatientID, DoctorID, AppointmentDate, and Status. Queries frequently filter by DoctorID (equality) and AppointmentDate (range) to retrieve a doctor's schedule. Currently, these queries are slow. Which index strategy will most improve performance for these queries?

Accepted Answer

Add a nonclustered index on (DoctorID, AppointmentDate).. A nonclustered index on (DoctorID, AppointmentDate) supports both equality filtering on DoctorID and range filtering on AppointmentDate. This index structure allows SQL Server to perform a single index seek for the doctor, then a range scan within that doctor's appointments, avoiding a full table scan. The order of columns matters: the leading column (DoctorID) handles the equality predicate, and the second column (AppointmentDate) handles the range predicate efficiently.

Answer

Add a clustered index on AppointmentID.

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Add a columnstore index on the Status column.

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Add a nonclustered index on (AppointmentDate, DoctorID).

Question 11

A smart home company stores sensor readings from thousands of devices in Azure Cosmos DB. Each reading includes a deviceID, timestamp (ISO format), sensor type, and value. The most common query retrieves all readings for a specific device within a time range. To minimize Request Units (RU) consumption and ensure even data distribution, which property should be chosen as the partition key?

Accepted Answer

A) deviceID. DeviceID is the correct partition key because it is the primary filter in the most common query (all readings for a specific device within a time range). Partitioning by deviceID ensures that all readings for a single device are stored in the same logical partition, making queries highly efficient by targeting a single partition. It also provides even data distribution across physical partitions, as thousands of devices will have roughly equal numbers of readings, minimizing RU consumption.

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B) timestamp

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C) sensor type

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D) value

Question 12

A bank processes a fund transfer transaction. The system debits $100 from Account A and then credits $100 to Account B. If the system crashes after debiting Account A but before crediting Account B, the database automatically reverts the debit. Which ACID property ensures this behavior?

Accepted Answer

Atomicity. Atomicity ensures that a transaction is treated as a single, indivisible unit of work. If any part of the transaction fails (e.g., a crash after debiting Account A but before crediting Account B), the entire transaction is rolled back, reverting any partial changes like the debit. This all-or-nothing behavior is the core of atomicity in database systems.

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Consistency

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Isolation

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Durability

Question 13

A logistics company stores shipping waybill data as JSON documents. Each document contains fields like 'shipmentId', 'destination', and 'items', but the number of items and the fields within each item can vary between shipments. Which category best describes this type of data?

Accepted Answer

Semi-structured data. JSON documents with varying fields and nested structures like 'items' that differ between shipments are a classic example of semi-structured data. Unlike structured data with a fixed schema, semi-structured data uses tags or markers (like JSON key-value pairs) to separate data elements, allowing for flexibility in the number and type of fields per record. This aligns with the DP-900 definition of semi-structured data, which includes formats such as JSON, XML, and Parquet.

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Operational data

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Unstructured data

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Structured data

Question 14

A social media application stores user profiles as JSON documents. Each user profile can have different attributes (e.g., some have 'education', others have 'work experience'). The application needs to query profiles by any attribute with low latency. Which Azure data store is most appropriate?

Accepted Answer

Azure Cosmos DB (SQL API). Azure Cosmos DB with the SQL API is the correct choice because it natively supports schema-agnostic JSON documents, allowing each user profile to have varying attributes without requiring a fixed schema. Its indexing policies enable low-latency queries on any attribute, and it provides single-digit millisecond response times for point reads and queries, which is essential for a social media application.

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Azure Blob Storage

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Azure Table Storage

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Azure SQL Database

Question 15

A social media startup needs to store user sessions as key-value pairs. Each session has a unique session ID, and the data needs to be globally distributed across multiple Azure regions to support low-latency reads for users worldwide. The development team expects heavy write throughput and needs flexible schema. Which Azure data store should they choose?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it provides globally distributed, multi-region writes and reads with turnkey global distribution, supports flexible schema via its document model, and offers multiple consistency levels to balance performance and data integrity. It is designed for high-throughput, low-latency workloads like user sessions, with session IDs serving as natural partition keys for efficient key-value lookups.

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Azure Table Storage

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Azure Blob Storage

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Azure Cache for Redis