DP-900 Describe an analytics workload on Azure Practice Question
A retail company uses Azure Data Lake Storage Gen2 to store raw clickstream data. They need to process this data using Azure Databricks to create hourly aggregated reports. The data pipeline must minimize costs while meeting a five-minute processing SLA. What is the most cost-effective compute option?
⚠ Common exam trap
Test-takers frequently confuse interactive clusters (always-on, for exploration) with job clusters (ephemeral, for automation), and assume that any 'pool' feature increases cost rather than reducing it, leading them to incorrectly select interactive clusters with autoscaling as the cheaper option.
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
✓
Use job clusters with pool-based allocation
Job clusters with pool-based allocation are the most cost-effective compute option for this scenario because job clusters are ephemeral—they start only when a job runs and terminate automatically after completion, eliminating idle costs. Pool-based allocation further reduces startup latency by maintaining a warm pool of pre-initialized VMs, enabling the pipeline to meet the five-minute SLA without paying for always-on compute.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use interactive clusters with autoscaling
Why it's wrong here
Interactive clusters with autoscaling are optimized for ad hoc exploration and collaborative notebook development, not for recurring production jobs. They remain warm between workloads to support interactivity, which incurs continuous per-second or per-minute costs even during idle gaps. Autoscaling only adjusts the number of worker nodes within a live cluster; it does not deallocate the cluster between scheduled runs. This makes them far more expensive and operationally inefficient than transient job clusters for retail data transformation jobs.
- ✓
Use job clusters with pool-based allocation
Why this is correct
Job clusters are ephemeral compute environments that start only for the duration of a scheduled Databricks job and terminate immediately afterward, preventing idle-hour costs. When backed by a pool, they can reuse pre-initialized idle VM instances, dramatically reducing cold-start latency and enabling lower per-node rates through pool-based allocation. This combination gives tight cost control and fast startup for repeated ETL workloads running against data in Azure Data Lake Storage Gen2, making it the correct choice for scheduled jobs.
- ✗
Use Azure Synapse Serverless SQL pools
Why it's wrong here
Azure Synapse Serverless SQL pools are a distributed query engine for executing on-demand T-SQL queries directly against data files in the data lake, priced per TB of data scanned. They do not provide the Databricks runtime, Spark APIs, or a cluster lifecycle management interface needed to run scheduled Databricks notebooks or JAR jobs. Attempting to use them for Databricks processing would be architecturally incorrect, because serverless SQL cannot execute Spark code or Databricks job workflows and has no concept of job clusters.
- ✗
Provision a dedicated SQL pool in Azure Synapse
Why it's wrong here
A dedicated SQL pool in Azure Synapse is a provisioned, persistent data warehouse cluster you pay for per hour regardless of query load, and it is designed for relational MPP query workloads, not general-purpose data transformation with Spark. It cannot run Databricks clusters or execute Databricks job definitions, and scaling or pausing it is separate from running scheduled Databricks jobs. For cost-effective Databricks compute on ADLS Gen2, this option introduces unrelated infrastructure and continuous charges, so it is the wrong answer.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data Lake Storage Gen2
Data Lake Storage Gen2 is a cloud-based storage service that combines a scalable data lake with enterprise-grade file system capabilities for big data analytics.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
About these practice questions
One of 820 original DP-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.