DP-900 Describe core data concepts Practice Question
A company is designing a data solution for a retail application. The solution must support real-time analytics on streaming sales data, and also provide historical reports for business intelligence. Which TWO data processing models should be combined to meet these requirements?
⚠ Common exam trap
Many candidates confuse 'distributed processing' (a general architecture) with a specific processing model, or they mistakenly think a transactional database can handle real-time analytics on streaming data, when in fact it is optimized for single-row transactions, not continuous data streams.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Batch processing
Batch processing (B) is correct because it is used to process large volumes of historical sales data at scheduled intervals, enabling the generation of comprehensive business intelligence reports. Stream processing (E) is correct because it handles real-time data ingestion and analytics on streaming sales data, allowing the application to react instantly to sales events. Combining these two models (often called a Lambda architecture) meets both the real-time and historical reporting requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Distributed processing
Why it's wrong here
Distributed processing describes splitting work across multiple nodes (e.g., Apache Spark, Hadoop), but it is a parallelism technique rather than a data processing pattern. The retail solution must choose whether to handle data in bulk batches or as a real-time stream; distribution alone does not specify scheduling, latency, or workload type, so it does not satisfy the requirement.
- ✓
Batch processing
Why this is correct
Batch processing collects and processes retail sales data over a defined time window (e.g., nightly, weekly), making it ideal for business intelligence reports that summarize historical trends. It offers high throughput, predictable costs, and easy recomputation/retry, which suits periodic reporting rather than immediate action. This matches the scenario's need to produce reports from accumulated transactional data.
- ✗
Data lake storage
Why it's wrong here
Data lake storage is a repository for raw, schema-on-read data (e.g., Azure Data Lake Storage Gen2), not a processing method. While a retail analytics solution may use a lake as a data source or landing zone, it does not determine whether ETL jobs run in batches or streams. Choosing storage without defining a processing pattern leaves the core requirement unanswered.
- ✗
Transactional database
Why it's wrong here
A transactional database (OLTP) is optimized for low-latency inserts, updates, and point lookups, often enforcing ACID guarantees for day-to-day retail operations. It is not designed for large-scale analytical scans and aggregations, which would degrade performance and are handled better by a data warehouse or analytical processing service. The scenario's reporting need calls for a processing model, not a transactional store.
- ✓
Stream processing
Why this is correct
Stream processing ingests and evaluates records as they arrive (e.g., Azure Stream Analytics, Kinesis) to deliver sub-second insights or trigger real-time actions. For a retail company generating periodic business intelligence reports from historical data, streaming's low latency is unnecessary and often more expensive per event. Thus, while valid for real-time dashboards, it is not the best fit for the stated batch-oriented reporting requirement.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Batch processing
Batch processing is a method of running high-volume, repetitive data jobs where a group of transactions is collected, processed together automatically, and then results are produced without real-time user interaction.
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