DP-900 Describe core data concepts Practice Test 9 — 15 Questions

Question 1

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 2

A global e-commerce platform uses a distributed database for its shopping cart service. The platform must be highly available and continue to accept writes even if network partitions occur between data centers. The business accepts that during a partition, users might see slightly outdated inventory counts, but the service must remain operational. According to the CAP theorem, which two properties is this system prioritizing?

Accepted Answer

Availability and Partition Tolerance. The scenario describes a system that must remain operational and accept writes during network partitions, even if data becomes temporarily inconsistent (stale inventory counts). This prioritizes Availability (the service stays up and accepts writes) and Partition Tolerance (the system continues to function despite network splits). According to the CAP theorem, when a partition occurs, a distributed system must choose between Consistency and Availability; here, the business accepts eventual consistency, so Availability and Partition Tolerance are the chosen properties.

Answer

Consistency and Partition Tolerance

Answer

Durability and Partition Tolerance

Question 3

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 4

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 5

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. Option A is correct because 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 6

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 system must continue accepting bids (writes) even during a network partition, which means it prioritizes Availability (A) — every request receives a response, even if it's not the most recent data. It also must function across multiple Azure regions that can become disconnected, which requires Partition Tolerance (P) — the system continues to operate despite network splits. The trade-off is that Consistency (C) is sacrificed, as users may see slightly outdated item prices during a partition. This is a classic AP (Availability and Partition Tolerance) choice from the CAP theorem.

Answer

Consistency (C) and Partition Tolerance (P)

Answer

Consistency (C) and Availability (A)

Question 7

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

Question 8

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

Question 9

Match each Azure data service to its primary purpose.

Question 10

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

Question 11

Match each Azure data migration tool to its use case.

Question 12

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 13

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. Option B is correct because 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 14

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 15

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