DP-900 Describe core data concepts Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Azure SQL Database serverless

  Why it's wrong here

  Azure SQL Database serverless is a PaaS relational database engine designed for fully managed, single-database workloads. While its serverless tier auto-scales compute and pauses idle databases, it does not provide a T-SQL query layer over external files in Azure Data Lake Storage. Its scope is transactional and relational storage, not in-place analytics over data lake files. Thus, it cannot satisfy the requirement to run complex analytics queries directly against a data lake.

• ✗

  Azure Analysis Services

  Why it's wrong here

  Azure Analysis Services is a BI semantic modeling engine that loads data into an in-memory tabular model, requiring an extract, transform, and load (ETL) process to bring data into the model. It does not allow ad-hoc or interactive T-SQL queries directly against files in a data lake; instead, it exposes pre-aggregated measures and relationships to client tools. Even with DirectQuery, it still relies on a relational data source rather than querying raw files. For this reason, it is not the correct serverless SQL-based option.

• ✗

  Azure Databricks

  Why it's wrong here

  Azure Databricks is an Apache Spark-based analytics platform that can read from Azure Data Lake Storage and process large-scale data using Spark SQL, DataFrames, or Python. However, its primary query language is Spark SQL, not T-SQL, and it typically requires you to manage or attach a Spark cluster to execute workloads. While it supports complex analytics, it does not offer a serverless T-SQL endpoint with pay-per-query pricing for data lake queries. Therefore, it does not match the specific requirement for a T-SQL interface over data lake storage.

• ✓

  Azure Synapse Serverless SQL pool

  Why this is correct

  Azure Synapse Serverless SQL pool is the correct service because it provides a serverless, on-demand T-SQL query engine that runs directly against files in Azure Data Lake Storage. It allows you to query data in place using standard T-SQL without provisioning or managing dedicated infrastructure, and you are billed only for the amount of data processed per query. It supports a variety of file formats such as Parquet, JSON, and CSV, enabling complex analytics and join operations across the data lake. This exactly meets the company's need for running complex analytics queries across petabyte-scale data with a familiar SQL interface.

Go deeper

Related to this question