DP-900 Quick Quiz — 10 Questions

Question 1

Which THREE of the following are characteristics of a data lake compared to a data warehouse?

Accepted Answer

Data lakes store data in its native or raw format.. A data lake stores data in its native or raw format, meaning it does not require transformation or schema definition at the time of ingestion. This allows organizations to retain the original fidelity of the data, which is a fundamental distinction from a data warehouse that typically transforms and structures data before loading (ETL). In Azure, Azure Data Lake Storage (ADLS) Gen2 supports storing any file format (e.g., Parquet, CSV, JSON, binary) without preprocessing.

Answer

Data lakes guarantee ACID transactions across all data.

Answer

Data lakes store only structured data.

Question 2

Refer to the exhibit. An administrator is configuring aggregations in Power BI Premium to improve performance on a large dataset. The aggregation is defined on the Sales table with SUM(Amount) grouped by ProductCategory, Region, and Date at the monthly level. However, some reports that query daily data are still slow. What is the most likely reason?

Accepted Answer

The aggregation level is monthly, but queries need daily granularity. The aggregation is defined at the monthly level (grouping by month), but the slow reports are querying daily data. Power BI aggregations work by pre-aggregating data at a specified granularity; when a query requests a lower level of detail (daily), the aggregation cannot satisfy the query, so Power BI falls back to scanning the full detailed dataset, causing slow performance. To improve daily queries, an additional aggregation at the daily level would be needed.

Answer

The dataset is too large for aggregations

Answer

The aggregation is not in DirectQuery mode

Answer

The aggregation has too many dimensions

Question 3

A company uses Azure SQL Database for an inventory management system. The Inventory table has millions of rows. Queries frequently filter on WarehouseID and then sort by LastUpdatedDate. The table currently has a clustered index on InventoryID (primary key). Which action will most improve query performance for these frequent filters?

Accepted Answer

Create a non-clustered index on (WarehouseID, LastUpdatedDate) INCLUDE (Quantity). A non-clustered index on (WarehouseID, LastUpdatedDate) allows the database engine to efficiently locate rows matching a specific WarehouseID and return them already sorted by LastUpdatedDate without accessing the clustered index (or with minimal lookup). Including the Quantity column as a non-key included column avoids key lookups for that column, further improving performance. Changing the clustered index to WarehouseID could cause fragmentation and is not ideal for uniqueness. A single-column index on LastUpdatedDate does not support the filter on WarehouseID. Partitioning by InventoryID does not help this query pattern.

Answer

Add a clustered index on WarehouseID

Answer

Create a non-clustered index on LastUpdatedDate only

Answer

Partition the table by InventoryID

Question 4

A company operates an online store where customers place orders and the system immediately updates inventory and records payments. This workload is best described as:

Accepted Answer

OLTP (Online Transaction Processing). This workload is best described as OLTP because it involves real-time, high-frequency transactions that immediately update inventory and record payments. OLTP systems are designed for concurrent, atomic operations that maintain data integrity, which is exactly what an online store's order processing requires.

Answer

OLAP (Online Analytical Processing)

Answer

Batch processing

Answer

Data warehousing

Question 5

A social media application stores user posts in Azure Cosmos DB. Each post has fields: PostID (unique), UserID, Timestamp, Content, LikesCount. The application frequently queries for all posts by a specific UserID ordered by Timestamp descending. To minimize Request Unit (RU) consumption, which partition key and indexing strategy should be used?

Accepted Answer

Partition key: UserID, and create a composite index on (UserID, Timestamp DESC). UserID is the most frequently filtered attribute, making it an ideal partition key to distribute data evenly and avoid cross-partition queries. Adding a composite index on (UserID, Timestamp DESC) allows the query to be served from a single physical partition with an index seek, minimizing RU consumption by avoiding a full scan or sort operation.

Answer

Partition key: Timestamp, and sort by UserID in the query

Answer

Partition key: PostID, and use ORDER BY Timestamp

Answer

Partition key: UserID, and use ORDER BY PostID

Question 6

A library management system uses Azure SQL Database. The Books table has 500,000 rows with columns: BookID (primary key, clustered), Title, Author, ISBN, PublishedYear, CopiesAvailable. Queries frequently filter by Author and then sort results by PublishedYear in descending order. Which indexing strategy will most improve query performance?

Accepted Answer

Create a nonclustered index on (Author, PublishedYear DESC) INCLUDE (CopiesAvailable, Title, ISBN).. It creates a covering index that matches the exact query pattern: filtering by Author and sorting by PublishedYear in descending order. By defining (Author, PublishedYear DESC) as the index key, SQL Server can perform an index seek on Author and an ordered scan on PublishedYear without a separate sort operation. Including the remaining columns (CopiesAvailable, Title, ISBN) as non-key columns makes the index covering, eliminating the need for key lookups to the clustered index.

Answer

Create a nonclustered index on (Author) INCLUDE (PublishedYear, CopiesAvailable, Title).

Answer

Create a nonclustered index on (PublishedYear) INCLUDE (Author).

Answer

Create a nonclustered columnstore index on (Author, PublishedYear, CopiesAvailable, Title, ISBN).

Question 7

A team is designing a data pipeline to process streaming sensor data from IoT devices. The data must be ingested, transformed in real time, and stored in a time-series database. Which combination of Azure services should they use?

Accepted Answer

Azure IoT Hub, Azure Stream Analytics, and Azure Data Explorer. Azure IoT Hub ingests streaming sensor data from IoT devices, Azure Stream Analytics provides real-time transformation and analysis of the data streams, and Azure Data Explorer (ADX) is a fully managed time-series database optimized for high-velocity telemetry data. This combination directly addresses the requirement for ingestion, real-time transformation, and time-series storage.

Answer

Azure IoT Hub, Azure Data Lake Storage, and Azure Databricks

Answer

Azure Event Hubs, Azure Functions, and Azure SQL Database

Answer

Azure Event Hubs, Azure Synapse Pipelines, and Azure Cosmos DB

Question 8

A company collects data from multiple sources: IoT sensor streams, social media feeds, and CSV files from legacy systems. They want to store all this data in its original format without any transformation, so that data scientists can later apply machine learning models or run ad-hoc queries. Which data storage pattern best describes this approach?

Accepted Answer

Data lake. A data lake is designed to store vast amounts of raw data in its native format (structured, semi-structured, or unstructured) without requiring upfront schema or transformation. This aligns perfectly with the scenario of ingesting IoT streams, social media feeds, and CSV files as-is, enabling data scientists to later apply machine learning or run ad-hoc queries directly against the raw data.

Answer

Data warehouse

Answer

Relational database

Answer

Data mart

Question 9

The exhibit shows a SQL query run against Azure SQL Database. What is the purpose of the HAVING clause in this query?

Accepted Answer

To filter groups based on aggregate conditions. The HAVING clause in SQL is used to filter groups after the GROUP BY clause has been applied, based on aggregate conditions such as SUM, COUNT, or AVG. In this query against Azure SQL Database, HAVING restricts the result to only those groups that satisfy the specified aggregate condition, which cannot be done with a WHERE clause because WHERE filters individual rows before grouping.

Answer

To filter rows before grouping

Answer

To sort the result set

Answer

To join two tables

Question 10

Which TWO of the following are common characteristics of a NoSQL database?

Accepted Answer

Flexible schema. NoSQL databases, such as MongoDB or Cassandra, use a flexible schema that allows documents or records to have varying fields without requiring predefined table structures. This enables developers to iterate quickly and store semi-structured or unstructured data, such as JSON documents, without costly schema migrations.

Answer

Normalized data storage

Answer

Strong ACID transaction support

Answer

Relational data model