DP-900 Describe core data concepts Practice Test 10 — 15 Questions

Question 1

A company is building a data lake and collects data from three sources: (1) a relational database exporting CSV files with fixed columns for customer records, (2) API responses stored as JSON files with varying fields for product reviews, and (3) scanned handwritten notes stored as TIFF images. Which statement correctly categorizes these data by structure type?

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

Accepted Answer

1: structured, 2: semi-structured, 3: unstructured. CSV files from a relational database have a fixed schema (rows and columns), making them structured data. JSON files from API responses with varying fields are semi-structured, as they use tags/keys to organize data without a rigid schema. TIFF images of handwritten notes are unstructured, lacking a predefined data model or organization.

Answer

1: semi-structured, 2: structured, 3: unstructured

Answer

1: structured, 2: unstructured, 3: semi-structured

Answer

1: unstructured, 2: semi-structured, 3: structured

Question 2

A data engineering team at a logistics company handles two distinct data processing workloads. The first workload ingests GPS data from delivery trucks every 10 seconds and updates a dashboard showing real-time vehicle locations. The second workload processes monthly CSV files of completed deliveries to generate reports on delivery times and route efficiency. Which statement correctly identifies these workloads?

Accepted Answer

GPS data processing is a streaming workload; monthly report processing is a batch workload. C is correct because GPS data ingested every 10 seconds is a continuous, near-real-time stream, making it a streaming workload. Monthly CSV file processing is a classic batch workload, as data is collected over a period and processed in a single, scheduled job. This distinction is fundamental in Azure data services: streaming workloads use services like Azure Stream Analytics or Event Hubs, while batch workloads use Azure Synapse Pipelines or Azure Data Factory.

Answer

Both workloads are streaming workloads

Answer

GPS data processing is a batch workload; monthly report processing is a streaming workload

Answer

Both workloads are batch workloads

Question 3

A data engineer is classifying data types collected from three sources for a data lake. Source 1: Customer records from a SQL database exported as CSV files with fixed columns (CustomerID, Name, Address). Source 2: Product reviews obtained via API as JSON documents with varying fields (e.g., some reviews include 'rating' and 'verified_purchase', others include 'comment'). Source 3: Scanned handwritten order forms saved as TIFF images. Which statement correctly categorizes these data by structure?

Accepted Answer

Source 1: Structured; Source 2: Semi-structured; Source 3: Unstructured. Source 1 (CSV from SQL) has a fixed schema with defined columns, making it structured data. Source 2 (JSON from API) allows varying fields per document, which is the hallmark of semi-structured data. Source 3 (TIFF images) contains no inherent schema or machine-readable structure, classifying it as unstructured data.

Answer

Source 1: Structured; Source 2: Structured; Source 3: Unstructured

Answer

Source 1: Semi-structured; Source 2: Structured; Source 3: Unstructured

Answer

Source 1: Structured; Source 2: Unstructured; Source 3: Semi-structured

Question 4

A manufacturing company collects sensor data from thousands of IoT devices. Each reading contains a device ID, timestamp, value, and device-specific measurement fields. The company needs to analyze the data in real time to detect anomalies and trigger alerts. They also need to store the same data for historical batch analysis to identify long-term trends. Which architecture pattern best describes this combination of data processing approaches?

Accepted Answer

Lambda architecture. The Lambda architecture is the correct pattern because it combines both stream processing for real-time anomaly detection and alerting, and batch processing for historical analysis of long-term trends. This architecture uses a speed layer for low-latency stream processing (e.g., Apache Kafka, Azure Stream Analytics) and a batch layer for comprehensive, accurate historical computations (e.g., Azure Data Lake, Apache Spark). The serving layer then merges results from both paths to provide a unified view.

Answer

Batch processing only

Answer

Stream processing only

Answer

Data lake

Question 5

A hospital collects patient data from multiple sources. Source A stores patient vitals as a continuous stream of readings from wearable devices. Source B stores historical medical records in a relational database with fixed columns (PatientID, Diagnosis, AdmissionDate). Source C stores doctor's notes as unstructured text files. Which statement correctly describes the structure of data from these sources?

Accepted Answer

Source A is structured, Source B is structured, Source C is unstructured.. Source A stores patient vitals as a continuous stream from wearable devices, which is structured data because it typically consists of time-stamped numeric readings with a fixed schema (e.g., timestamp, heart rate, blood pressure). Source B uses a relational database with fixed columns (PatientID, Diagnosis, AdmissionDate), which is classic structured data. Source C contains unstructured text files (doctor's notes) with no predefined schema. Therefore, Option B correctly identifies all three sources.

Answer

Source A is semi-structured, Source B is structured, Source C is unstructured.

Answer

Source A is structured, Source B is unstructured, Source C is semi-structured.

Answer

Source A is semi-structured, Source B is semi-structured, Source C is unstructured.

Question 6

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?

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 7

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 8

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

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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 9

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 10

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 11

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 12

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 13

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 14

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 15

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