Cracking Azure Synapse Interviews: Most Frequently Asked Questions

It was a bright Monday morning when Aryan, a passionate data engineer, walked into his virtual interview for an Azure Synapse role.

The stakes were high — this role promised him the opportunity to work on cutting-edge data architectures. As he sipped his coffee, Aryan reflected on his preparation, recalling the questions he had practiced.

Little did he know, the interviewer was about to dive deep into the very heart of Synapse Analytics.

Chapter 1: The Mystery of Tumbling Window Triggers

The interviewer leaned in, “Aryan, can you explain what a Tumbling Window Trigger is and how it differs from a Scheduled Trigger?”

Aryan smiled. “Imagine a conveyor belt in a chocolate factory. Each box of chocolates represents a ‘window’ — the belt moves, but the boxes don’t overlap. That’s a Tumbling Window Trigger — fixed, non-overlapping intervals. Perfect for incremental data loads.”

“And how about Scheduled Triggers?” the interviewer probed.

“Scheduled Triggers are like alarm clocks. They go off at specific times, but they don’t care what happened before. No history tracking. Tumbling windows, however, remember and ensure each batch is processed exactly once.”

Aryan even mentioned backfilling: “If I need to process historical data, I set the start and end dates using SQL’s BETWEEN clause:

SELECT * FROM sales_data 
WHERE transaction_date BETWEEN '2024-01-01' AND '2024-01-31';

It’s like asking the conveyor belt to rewind and process past boxes.”

The interviewer nodded, clearly impressed.

Chapter 2: The Duel Between Synapse SQL and Azure SQL

Next came a curveball: “How does Synapse SQL differ from Azure SQL?”

Aryan chuckled softly, “Think of Azure SQL as a sports car — fast, sleek, optimized for quick, transactional tasks (OLTP). Now, Synapse SQL? That’s a freight train — built for heavy loads, optimized for analytical processing (OLAP).”

He elaborated:

• Synapse SQL uses Massively Parallel Processing (MPP), distributing queries across multiple nodes.
• Azure SQL relies on Symmetric Multi-Processing (SMP) — great for smaller, transactional workloads.

Workload Management? “In Synapse, we create workload groups to prioritize tasks. It’s like assigning VIP lanes on a highway.”

Aryan even touched on distribution methods:

• Round Robin: Like dealing cards randomly.
• Hash Distribution: Data organized based on specific attributes.
• Replicated Tables: Entire tables copied to all nodes for faster joins.

And the cherry on top? Columnar Indexes. “Synapse loves columnstore indexes for big data crunching, while Azure SQL sticks to traditional row-based indexing.”

Chapter 3: The Enigma of DWUs

“What DWU configuration did your team use?”

Aryan responded, “DWUs — or Data Warehouse Units — are the horsepower behind Synapse. We scaled DWUs based on workload needs, balancing cost and performance. For heavy ETL pipelines, we’d ramp up the DWUs; for lighter tasks, we’d dial them back. It’s all about efficiency.”

Chapter 4: PolyBase and the Serverless Dilemma

“Can you use PolyBase with Azure Serverless SQL?”

Aryan grinned. “No, PolyBase is exclusive to Dedicated SQL Pools. It’s like trying to install a racing engine in a bicycle — not compatible.”

Serverless SQL vs. Dedicated SQL?

• Serverless SQL is pay-per-query — perfect for ad-hoc analysis.
• Dedicated SQL Pools offer reserved resources for heavy, consistent workloads.

Drawbacks? “Serverless struggles with large datasets and has limitations like restricted external table support. Dedicated pools handle these with ease.”

The next question from interviewer is “How does PolyBase differ between Dedicated and Serverless SQL Pools?”

Aryan summarized:

• Dedicated SQL Pools: Full PolyBase support, optimized for high-speed data loading.
• Serverless SQL Pools: No PolyBase support; relies on external tables with performance trade-offs.

Chapter 5: The Data Transfer Odyssey

“Walk me through copying data from on-prem to Azure using Synapse.”

Aryan painted the picture:

1. Self-Hosted Integration Runtime (SHIR): “We set it up on a VM to bridge on-prem data to the cloud.”
2. VNET Integration: “Ensured secure data transfer within our private network.”
3. Synapse Pipelines: “Managed data flows seamlessly.”

Chapter 6: The Excel Conundrum

“You’re reading Excel data but want to skip the first few rows while showing all rows in the preview. How?”

Aryan replied confidently, “Use Data Flows in Synapse Pipelines. In the source settings, configure row skipping. The preview pane still displays all rows because it samples raw data, not the transformed output.”

The Interview Ends

As the call wrapped up, the interviewer smiled. “Aryan, your answers weren’t just technically sound — they told a story.”

Aryan logged off, confident he’d nailed it. Because interviews aren’t just about facts — they’re about the story you tell.

Moral of the story? Next time you prepare for an Azure Synapse interview, don’t just memorize answers. Understand them. Relate them to real-world scenarios. And most importantly, tell your story.