DP-900 Describe core data concepts Practice Test 11 — 15 Questions

Question 1

A company collects data from three sources: Source A: Customer records from a relational database with fixed columns (CustomerID, Name, Address). Source B: Social media posts in JSON format with varying fields (e.g., some posts have 'likes', others have 'shares'). Source C: Handwritten notes saved as scanned images in TIFF format. Which statement correctly categorizes the data by structure?

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

Accepted Answer

Source A: Structured, Source B: Semi-structured, Source C: Unstructured. Source A's relational database with fixed columns (CustomerID, Name, Address) enforces a strict schema, making it structured data. Source B's JSON format allows varying fields like 'likes' or 'shares' per record, which is the hallmark of semi-structured data (self-describing, schema-on-read). Source C's scanned TIFF images are binary blobs with no inherent internal structure for querying, classifying them as unstructured data. This matches the standard DP-900 categorization: structured (fixed schema), semi-structured (flexible schema), unstructured (no schema).

Answer

Source A: Structured, Source B: Unstructured, Source C: Semi-structured

Answer

Source A: Semi-structured, Source B: Structured, Source C: Unstructured

Answer

Source A: Semi-structured, Source B: Unstructured, Source C: Structured

Question 2

A financial analytics company has two distinct data processing workloads. The first workload ingests real-time stock trade data from a message queue, calculates moving averages every minute, and updates a dashboard for traders. The second workload receives daily CSV files containing end-of-day trade summaries, transforms them using Python scripts, and loads the results into a data warehouse for monthly reporting. Which statement correctly characterizes these workloads?

Accepted Answer

First workload: Stream processing, Second workload: Batch processing. The first workload processes real-time stock trade data from a message queue and calculates moving averages every minute, which is a classic stream processing pattern (continuous, low-latency data ingestion and computation). The second workload handles daily CSV files with end-of-day summaries, transforms them with Python scripts, and loads results into a data warehouse for monthly reporting, which is a classic batch processing pattern (scheduled, high-latency processing of bounded data sets).

Answer

First workload: Batch processing, Second workload: Stream processing

Answer

First workload: OLTP, Second workload: OLAP

Answer

First workload: Transactional processing, Second workload: Analytical processing

Question 3

A globally distributed online auction platform uses a replicated database system across multiple Azure regions. The system must continue accepting bids (writes) even if a network partition occurs between regions, because auctions cannot be interrupted. The business decides that during a partition, some users might see slightly outdated item prices (read inconsistency) but all bids must be recorded. According to the CAP theorem, which two properties is this system prioritizing?

Accepted Answer

Availability (A) and Partition Tolerance (P). The CAP theorem defines three properties: Consistency (C), Availability (A), and Partition Tolerance (P). The system continues accepting bids during network partitions, prioritizing Availability (every request receives a response) and Partition Tolerance (system operates despite network splits). Option A (Availability and Partition Tolerance) directly describes this AP trade-off. Option D (Durability and Availability) is incorrect because Durability is not a CAP property; it belongs to ACID. The question specifically asks for CAP properties, so only A is correct.

Answer

Consistency (C) and Partition Tolerance (P)

Answer

Consistency (C) and Availability (A)

Answer

Durability and Availability

Question 4

Drag and drop the steps to create an Azure SQL Database in the correct order.

Accepted Answer

Step 1: Select SQL Database service, Step 2: Configure server (create new server), Step 3: Configure database settings (name, pricing tier, compute+storage), Step 4: Review and create. Creating an Azure SQL Database involves selecting the service, configuring the server and database settings, choosing the appropriate tier, and finally deploying.

Answer

Step 1: Configure server (create new server), Step 2: Select SQL Database service, Step 3: Configure database settings (name, pricing tier, compute+storage), Step 4: Review and create

Answer

Step 1: Select SQL Database service, Step 2: Configure database settings (name, pricing tier, compute+storage), Step 3: Configure server (create new server), Step 4: Review and create

Answer

Step 1: Configure database settings (name, pricing tier, compute+storage), Step 2: Select SQL Database service, Step 3: Configure server (create new server), Step 4: Review and create

Question 5

Drag and drop the steps to create an Azure Data Lake Storage Gen2 account in the correct order.

Accepted Answer

Step 1: Create a storage account with Standard performance and Locally-redundant storage. Step 2: On the Advanced tab, enable hierarchical namespace for Data Lake Storage Gen2. Step 3: Configure networking and data protection settings. Step 4: Review the configuration and create the storage account.. Creating ADLS Gen2 requires enabling the hierarchical namespace feature on a standard storage account.

Answer

Step 1: On the Advanced tab, enable hierarchical namespace. Step 2: Create a storage account with Standard performance and Locally-redundant storage. Step 3: Configure networking and data protection settings. Step 4: Review and create.

Answer

Step 1: Create a storage account with Standard performance and Locally-redundant storage. Step 2: Configure networking and data protection settings. Step 3: On the Advanced tab, enable hierarchical namespace. Step 4: Review and create.

Answer

Step 1: Create a storage account with Standard performance and Locally-redundant storage. Step 2: On the Advanced tab, enable hierarchical namespace. Step 3: Review and create. Step 4: Configure networking and data protection settings.

Question 6

Match each Azure data service to its primary purpose.

Accepted Answer

Azure SQL Database: Relational database as a service for structured data.. Azure SQL Database is for relational data, Azure Cosmos DB is a NoSQL globally distributed database, Azure Blob Storage stores unstructured objects, and Azure Synapse Analytics handles big data analytics and warehousing.

Answer

Azure SQL Database: NoSQL database with global distribution.

Answer

Azure Cosmos DB: Object storage for unstructured data.

Question 7

Match each Azure Cosmos DB API to its supported data model.

Accepted Answer

SQL API: Document. Azure Cosmos DB APIs map to specific data models: SQL and MongoDB for document, Cassandra for wide-column, Gremlin for graph, and Table for key-value. Common confusions involve misassigning Gremlin and Table.

Answer

Gremlin API: Key-value

Answer

Table API: Graph

Question 8

Match each Azure data migration tool to its use case.

Accepted Answer

Azure Migrate: Assesses on-premises servers for migration to Azure. Azure Migrate is for server assessment, Azure Database Migration Service is for database migration, and Azure Data Box is for offline data transfer. Common confusions involve swapping assessment and migration roles.

Answer

Azure Migrate: Migrates databases to Azure with minimal downtime

Answer

Azure Database Migration Service: Assesses on-premises servers for migration to Azure

Answer

Azure Data Box: Assesses on-premises servers for migration

Question 9

A company needs to store JSON documents that are frequently updated by multiple services. The solution must support indexing and querying by any property. Which Azure data service should they use?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is a fully managed NoSQL database designed for JSON documents, offering native support for indexing every property automatically without requiring a predefined schema. Its multi-model API (including SQL API) allows querying by any property with low-latency reads and writes, making it ideal for services that frequently update JSON documents.

Answer

Azure Blob Storage

Answer

Azure SQL Database

Answer

Azure Table Storage

Question 10

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 11

A data engineer needs to implement a solution that provides near real-time analytics on clickstream data. The data arrives as JSON events and must be queryable with sub-second latency using SQL-like queries. The solution should minimize operational overhead. Which Azure service should they use?

Accepted Answer

Azure Data Explorer. Azure Data Explorer (ADX) is designed for interactive analytics on large volumes of streaming and historical data with sub-second query latency using Kusto Query Language (KQL), which supports SQL-like syntax. It natively ingests JSON events, provides near real-time analytics, and minimizes operational overhead as a fully managed, serverless service.

Answer

Azure Stream Analytics

Answer

Azure Analysis Services

Answer

Azure Synapse Analytics

Question 12

Your organization has a large dataset of customer transactions stored in Azure Blob Storage as CSV files. You need to run ad-hoc SQL queries on this data without loading it into a database. Which Azure service should you use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool allows you to query data directly from files in Azure Blob Storage using standard T-SQL syntax, without needing to load or move the data into a database. It uses a pay-per-query model and supports CSV, Parquet, and JSON formats, making it ideal for ad-hoc analytical queries over large datasets stored in data lakes.

Answer

Azure Data Factory

Answer

Azure SQL Database

Answer

Azure Analysis Services

Question 13

A company is designing a data solution for their e-commerce platform. They need to store product catalogs with varying attributes, support high-throughput read/write operations, and ensure low-latency access globally. Which Azure data store is most appropriate?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the most appropriate choice because it is a globally distributed, multi-model database service that supports schema-agnostic storage of product catalogs with varying attributes. It offers guaranteed single-digit-millisecond latency for reads and writes at any scale, and its turnkey global distribution enables low-latency access from multiple regions, meeting the e-commerce platform's high-throughput and global requirements.

Answer

Azure SQL Database

Answer

Azure Redis Cache

Answer

Azure Data Lake Storage

Question 14

You are implementing a data pipeline that ingests millions of events per second from IoT devices. The pipeline must tolerate failures and guarantee exactly-once processing. Which Azure service should you use to ingest the events?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct choice because it is a big data streaming platform and event ingestion service designed for high-throughput scenarios, capable of ingesting millions of events per second. It supports exactly-once processing through checkpointing and partition-based offset management, and its built-in replication and availability zones provide fault tolerance.

Answer

Azure IoT Hub

Answer

Azure Service Bus

Answer

Azure Queue Storage

Question 15

A company wants to run complex analytics queries across petabytes of data stored in Azure Data Lake Storage. They need a serverless option that supports T-SQL. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it provides a serverless, on-demand query service that allows you to run T-SQL queries directly against data stored in Azure Data Lake Storage (ADLS). It supports complex analytics over petabytes of data without provisioning any infrastructure, and it uses T-SQL as the query language, meeting all the stated requirements.

Answer

Azure SQL Database serverless

Answer

Azure Analysis Services

Answer

Azure Databricks