DP-900 Practice Test 6 — 10 Questions

Question 1

A company uses Azure Synapse Analytics to run complex queries against large datasets stored in Parquet files in Azure Data Lake Storage Gen2. They notice that queries scanning entire partitions are slow due to high I/O overhead on the compute nodes. Investigation shows each daily partition contains thousands of small files (under 1 MB each). Which optimization should be implemented first to improve query performance?

Accepted Answer

Compact small files into larger ones before querying. Option C is correct because the high I/O overhead is caused by the thousands of small files per partition. When Synapse compute nodes read many small files, the overhead of opening, reading metadata, and closing each file dominates, even though the total data volume is small. Compacting these small files into fewer, larger files (e.g., 128 MB or more) reduces the number of file operations, improves read throughput, and allows more efficient predicate pushdown and parallelism.

Answer

Increase the number of compute nodes

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Use columnstore indexes on external tables

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Change the partition column to a different date granularity

Question 2

A financial services company stores account balances in Azure SQL Database (strong consistency) and transaction audit logs in Azure Cosmos DB (eventual consistency by default). A compliance requirement demands that when a transaction is rolled back in the SQL database, the corresponding audit log entries in Cosmos DB must also be removed within a short time frame. Which term best describes the difficulty of maintaining this constraint?

Accepted Answer

Distributed transaction coordination. Option C is correct because the scenario requires coordinating a rollback across two distinct data stores—Azure SQL Database (ACID-compliant, strong consistency) and Azure Cosmos DB (eventual consistency by default). This cross-system transactional consistency is a classic distributed transaction coordination problem, often addressed via patterns like the two-phase commit (2PC) or the saga pattern, but not natively supported between these two services without custom orchestration.

Answer

ACID compliance

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Idempotency

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Schema flexibility

Question 3

A financial application requires strict consistency and transaction support (ACID). Which Azure data service is most appropriate for storing its core transactional data?

Accepted Answer

Azure SQL Database. Azure SQL Database is a fully managed relational database service that provides full ACID (Atomicity, Consistency, Isolation, Durability) transaction support through its SQL Server engine. It is the correct choice for a financial application requiring strict consistency and transactional integrity, as it guarantees that all transactions are processed reliably and adhere to the ACID properties.

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Azure Cosmos DB

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Azure Table Storage

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Azure Data Lake Storage

Question 4

A development team is designing an application that stores user session data in Azure Cosmos DB. Each session document contains a sessionId (unique), userId, timestamp, and a JSON field 'metadata' that can include various optional properties. The application frequently queries by userId to retrieve all sessions for a particular user. Which property should be chosen as the partition key to optimize query performance and ensure even data distribution?

Accepted Answer

userId. The partition key should be the property most frequently used in queries and that provides high cardinality for even distribution. Since the application frequently queries by userId to retrieve all sessions for a user, choosing userId as the partition key ensures that all session documents for a given user are stored in the same logical partition, making these queries efficient and fast. Additionally, userId typically has a large number of distinct values, which promotes even data distribution across physical partitions.

Answer

sessionId

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timestamp

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metadata

Question 5

A financial services company stores transaction data in Azure Data Lake Storage Gen2 as Parquet files, partitioned by date. The data volume is 5 TB per day. The analytics team runs ad-hoc SQL queries to detect fraudulent patterns. Queries are highly selective (filtering on AccountID and date range). The team also needs to create external tables and views for use in Power BI. They want to pay only for the data processed by each query and avoid provisioning any compute resources. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without provisioning any compute resources. It charges per terabyte of data processed, aligning with the requirement to pay only for data scanned by each query. It also supports creating external tables and views for Power BI, making it ideal for ad-hoc, selective queries on partitioned Parquet files.

Answer

Azure Databricks with interactive clusters

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Azure Stream Analytics

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Azure HDInsight with Spark

Question 6

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.

Answer

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

Answer

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

Answer

Use Azure Data Explorer to query the data lake.

Question 7

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 8

A manufacturing company stores IoT sensor data as blobs in Azure Blob Storage. Each blob is named with a device ID and a timestamp, and they need to quickly find all blobs for a specific device within a date range. Which Azure Blob Storage feature should they use to query blobs based on custom metadata?

Accepted Answer

Blob index tags. Blob index tags allow you to apply custom key-value metadata to blobs and then query them using a filtered query across containers or storage accounts. This enables efficient retrieval of blobs by device ID and timestamp without scanning all blob names or maintaining a separate index.

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Blob snapshots

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Blob soft delete

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Blob lifecycle management

Question 9

A global gaming company stores player profiles in Azure Cosmos DB. Each profile document contains PlayerID (unique), PlayerName, Email, and a nested array of Achievements. The most common query is to look up a player by PlayerID and retrieve their achievements. The company needs strong consistency for reads and writes to ensure that when a player earns an achievement, it is immediately visible. Which partition key and consistency level should they choose?

Accepted Answer

B. Partition key: PlayerID; Consistency: Strong. Option B is correct because PlayerID is the natural partition key for the most common query (lookup by PlayerID), ensuring efficient single-partition queries. Strong consistency is required to guarantee that when a player earns an achievement, the write is immediately visible to all subsequent reads, which is critical for the gaming scenario.

Answer

A. Partition key: PlayerID; Consistency: Eventual

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C. Partition key: Achievements; Consistency: Strong

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D. Partition key: Email; Consistency: Bounded staleness

Question 10

A gaming company stores player profiles in Azure Cosmos DB using the NoSQL API. Each profile is a JSON document containing fields like playerId, userName, level, inventory (an array of items), and friends (an array of playerIds). The application frequently needs to query all players that have a specific item in their inventory (e.g., 'sword'). Which Cosmos DB feature should they use to support this query efficiently?

Accepted Answer

Indexing policy with a wildcard index. Option D is correct because a wildcard index in the indexing policy allows Azure Cosmos DB to automatically index all properties within a JSON document, including nested array elements like those in the 'inventory' array. This enables efficient queries such as 'SELECT * FROM c WHERE ARRAY_CONTAINS(c.inventory, {name: "sword"})' without requiring a custom composite index for each possible item. Without a wildcard index, the query would require a full scan of all documents, which is inefficient at scale.

Answer

Change feed

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Stored procedures

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Composite index