Azure Data Factory Scenario-Based Interview Questions – Day 9
Sai Raj Ghantasala
DATA ENGINEER | DATA ANALYST | DEVOPS | BUSINESS ANALYST | AZURE | BI | BLUE PRISM | UI PATH
🔹 Scenario 91
Handling Dynamic Data Partitioning for Large-Scale Ingestion Your pipeline ingests terabytes of data daily from multiple sources, and you need to dynamically partition the output based on runtime metadata (e.g., source system, ingestion date). How would you design this in ADF?
Answer:
Use a Get Metadata activity to retrieve source metadata (e.g., source_system, ingestion_date) from a configuration table or file headers.
In a Mapping Data Flow, configure a Sink transformation with dynamic partitioning, using an expression like concat(source_system, '/', year(ingestion_date), '/', month(ingestion_date)) to create partitions (e.g., systemA/2025/05/).
Parameterize the sink dataset to support dynamic folder paths, ensuring scalability across sources.
Optimize performance by increasing Azure IR compute and monitoring partition sizes with Azure Monitor to avoid skew.
🔹 Scenario 92
Implementing Secure Data Sharing with External Partners You need to share processed data from ADLS Gen2 with external partners while ensuring strict access control and encryption. How would you implement this in ADF?
Answer:
Use Azure Data Share to create a secure sharing mechanism, integrated with ADF for automated data publishing.
Configure a Copy Activity to move processed data to a shared ADLS Gen2 container, encrypted with Customer-Managed Keys stored in Azure Key Vault.
Apply Azure RBAC and Shared Access Signatures (SAS) to grant partners time-bound, read-only access to specific datasets.
Log sharing activities to a control table via a Stored Procedure activity and audit access with Azure Monitor Logs for compliance.
🔹 Scenario 93
Processing Real-Time Data with Complex Event Processing Your pipeline processes streaming data from Azure Event Hubs, requiring complex event processing (e.g., sessionization, pattern matching). How would you design this?
Answer:
Use Azure Stream Analytics to process Event Hubs data, implementing windowing functions (e.g., session windows) and pattern matching with MATCH_RECOGNIZE for complex logic.
Output processed events to a staging area in ADLS Gen2, triggering an ADF pipeline with an Event Trigger to load results into a sink (e.g., Azure SQL).
Configure Stream Analytics with sufficient Streaming Units to handle high throughput, monitored via Azure Monitor.
Use a Mapping Data Flow in ADF for post-processing if additional transformations are needed.
🔹 Scenario 94
Handling Pipeline Failures with Automated Remediation Your pipeline fails due to intermittent data quality issues (e.g., missing columns). How would you automate remediation to minimize downtime?
Answer:
Implement a Try-Catch pattern using an If Condition activity to detect failures, capturing errors like missingColumn from the activity’s output.
Use a Lookup activity to retrieve remediation rules from a configuration table (e.g., missingColumn → add default value).
Apply fixes in a Mapping Data Flow with Derived Column transformations (e.g., coalesce(column, 'default')) and retry the failed step via Execute Pipeline.
Notify persistent issues with Azure Monitor alerts, logging remediation actions to a control table.
🔹 Scenario 95
Optimizing Pipelines for Cost and Performance Your pipeline processes variable workloads, and you need to balance cost and performance across peak and off-peak periods. How would you optimize this?
Answer:
Use an AutoResolveIntegrationRuntime to dynamically scale compute based on workload, reducing costs during off-peak times.
Implement incremental loads with watermark columns to process only new/changed data, minimizing resource usage.
Schedule heavy pipelines during off-peak hours using Tumbling Window Triggers to leverage lower Azure rates.
Track costs with Azure Cost Management, setting alerts for spikes and optimizing Data Integration Units (DIUs) based on Azure Monitor performance metrics.
🔹 Scenario 96
Handling Multi-Cloud Data Integration Your pipeline needs to integrate data from AWS S3 into Azure Synapse Analytics. How would you design this cross-cloud integration?
Answer:
Configure an Amazon S3 Linked Service in ADF using access keys stored in Azure Key Vault for secure access.
Use a Copy Activity to stage S3 data in ADLS Gen2, leveraging staged copy to optimize transfer.
Load staged data into Synapse Analytics with another Copy Activity, using PolyBase for high-performance bulk loading.
Monitor cross-cloud transfers with Azure Monitor and secure data in transit with SSL encryption.
🔹 Scenario 97
Implementing Data Retention Policies Your pipeline must enforce data retention policies, deleting data older than 90 days from ADLS Gen2. How would you implement this?
Answer:
Use a Get Metadata activity with a wildcard path (e.g., data/year=*/month=*/day=*) to list files, filtering by lastModified date.
Pass files older than 90 days (e.g., @lesser(activity('GetMetadata').output.lastModified, adddays(utcnow(), -90))) to a Delete Activity.
Log deletions to a control table via a Stored Procedure activity for audit purposes.
Schedule the pipeline daily with a Scheduled Trigger and monitor with Azure Monitor for compliance.
🔹 Scenario 98
Processing Data with Conditional Aggregation Your pipeline must aggregate data based on dynamic conditions (e.g., group by region for some records, by product for others). How would you implement this?
Answer:
Use a Mapping Data Flow with a Conditional Split transformation to route records based on conditions (e.g., type == 'region' → group by region).
Apply Aggregate transformations to each branch, defining group-by fields dynamically (e.g., region or product).
Union the results with a Union transformation and sink to a target (e.g., ADLS Gen2 or SQL).
Optimize by partitioning data by the group-by key, monitored with Data Preview for accuracy.
🔹 Scenario 99
Handling Pipeline Dependencies Across Azure Tenants Your pipeline depends on data processed in a different Azure tenant. How would you coordinate this cross-tenant dependency?
Answer:
Use a Web Activity to call a REST API exposed by the other tenant’s ADF, checking the status of the dependent pipeline.
Store credentials for the API in Azure Key Vault, using a Managed Identity for secure access.
Implement an Until activity to poll the API until the dependency is met, with a timeout to avoid infinite loops.
Trigger the local pipeline with a Copy Activity once the dependency is confirmed, logging coordination details to a control table.
🔹 Scenario 100
Implementing Real-Time Monitoring with Custom Dashboards Your organization requires a custom dashboard to monitor pipeline performance in real-time. How would you implement this with ADF?
Answer:
Log pipeline metrics (e.g., run duration, rows processed, errors) to a control table in Azure SQL using Stored Procedure activities.
Use Azure Monitor to collect ADF metrics (e.g., pipeline runs, activity failures) and export them to Azure Log Analytics.
Build a Power BI dashboard connected to the control table and Log Analytics, visualizing KPIs like success rate and latency.
Automate data refreshes with Power BI Dataflows and set Azure Monitor alerts for critical issues, ensuring real-time updates.
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