Data Engineering Interview Questions: Accenture

As the demand Data Engineering continues to grow, companies like Accenture are keen on assessing candidates’ knowledge of data integration, management, and analysis. Below are some of the key interview questions you might encounter during the interview process for a data engineering role at Accenture, along with detailed explanations to help you prepare effectively.

Key Interview Questions (Data Engineering)

Q1. Which Integration Runtime (IR) Should Be Used for Copying Data from an On-Premise Database to Azure?

The Self-hosted Integration Runtime (IR) is typically used for copying data from an on-premises database to Azure. This IR can access on-premises data sources securely and move data to cloud destinations like Azure Data Lake or Azure Blob Storage.

Q2. Explain the Differences Between a Scheduled Trigger and a Tumbling Window Trigger in Azure Data Factory. When Would You Use Each?

Scheduled Trigger: Fires based on a defined time schedule (e.g., hourly, daily). It’s useful for running pipelines at specific intervals.
Tumbling Window Trigger: Fires on fixed-size time intervals that do not overlap. Each window runs independently and is ideal for time-based data processing tasks where data arrives in chunks.

Use Case:

Use a Scheduled Trigger for routine data refreshes (e.g., nightly data loads).
Use a Tumbling Window Trigger for processing streams of data, such as every hour’s worth of logs.

Q3. What Is Azure Data Factory (ADF), and How Does It Enable ETL and ELT Processes in a Cloud Environment?

Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation.

ETL (Extract, Transform, Load): In this process, data is first extracted from various sources, transformed into a desired format, and then loaded into a destination.
ELT (Extract, Load, Transform): Data is first extracted and loaded into a data warehouse or data lake, then transformed as needed. This is often used in big data scenarios.

ADF supports both processes by providing tools for data movement, transformation (using Data Flow activities), and orchestration through pipelines.

Q4. Describe Azure Data Lake and Its Role in a Data Architecture. How Does It Differ from Azure Blob Storage?

Azure Data Lake is a scalable data storage and analytics service that is designed for big data analytics. It allows for the storage of vast amounts of unstructured, semi-structured, and structured data.

Role in Data Architecture:

Centralized repository for large volumes of data.
Supports analytics workloads using tools like Azure Databricks, Azure Synapse Analytics, etc.

Differences from Azure Blob Storage:

Azure Data Lake is optimized for analytics workloads and provides features like hierarchical namespace and fine-grained access control.
Azure Blob Storage is more general-purpose storage that can store a variety of data types but lacks the analytical optimizations present in Azure Data Lake.

Q5. What Is an Index in a Database Table? Discuss Different Types of Indexes and Their Impact on Query Performance.

An index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional space and slower write operations.

Types of Indexes:

Clustered Index: Sorts and stores the data rows in the table based on the key values. Only one clustered index can be created per table.
Non-Clustered Index: Contains a pointer to the actual data rows in the table. Multiple non-clustered indexes can exist on a table.
Unique Index: Ensures that the values in the indexed column are unique.

Impact on Query Performance:

Indexes speed up data retrieval but can slow down data insertion, updates, and deletions.
Proper indexing strategies can significantly enhance query performance.

Q6. Given Two Datasets, Explain How the Number of Records Will Vary for Each Type of Join (Inner Join, Left Join, Right Join, Full Outer Join).

Inner Join: Returns only the records that have matching values in both datasets. Number of records = matching records.
Left Join: Returns all records from the left dataset and the matched records from the right dataset. If there are no matches, NULLs are returned for columns from the right dataset. Number of records = records in left dataset.
Right Join: Returns all records from the right dataset and the matched records from the left dataset. Similar to Left Join, but the right dataset is prioritized. Number of records = records in right dataset.
Full Outer Join: Returns all records when there is a match in either left or right dataset. Number of records = total records in both datasets, with NULLs where there are no matches.

Q7. What Are the Control Flow Activities in Azure Data Factory? Explain How They Differ from Data Flow Activities and Their Typical Use Cases.

Control Flow Activities orchestrate the execution of other activities in Azure Data Factory. Common control flow activities include:

Execute Pipeline: Executes a pipeline.
ForEach: Iterates over a collection of items and executes activities for each item.
If Condition: Executes activities based on a condition.

Differences:

Control Flow Activities manage workflow and orchestration.
Data Flow Activities focus on data transformation and processing.

Use Cases:

Use Control Flow Activities for managing dependencies, branching logic, and looping through datasets.
Use Data Flow Activities for complex data transformations such as joins, aggregations, and data type conversions.

Q8. Discuss Key Concepts in Data Modeling, Including Normalization and Denormalization. How Do Security Concerns Influence Your Choice of Synapse Table Types in a Given Scenario? Provide an Example of a Scenario-Based ADF Pipeline.

Data Modeling Concepts:

Normalization: The process of organizing data to reduce redundancy and improve data integrity. It involves dividing a database into tables and defining relationships.
Denormalization: The process of combining tables to improve read performance, which can lead to data redundancy.

Security Concerns:

When choosing Synapse table types (e.g., dedicated SQL pool vs. serverless SQL pool), consider the sensitivity of data. Denormalized tables might expose sensitive data more easily than normalized tables.

Example Scenario:
An ADF pipeline to ingest customer data for analytics might use normalized tables in a dedicated SQL pool to ensure data integrity while enforcing strict access controls. After data is ingested, a separate denormalized table can be created for analytical queries that require faster read access.

Q9. What Are the Different Types of Integration Runtimes (IR) in Azure Data Factory? Discuss Their Use Cases and Limitations.

Types of Integration Runtimes (IR):

Azure IR: Used for data movement and transformation in the cloud. Ideal for connecting to cloud data sources.

Use Case: Copying data between Azure services or from cloud to on-premises.
Limitations: Cannot access on-premises data sources.

Self-hosted IR: Used to connect to on-premises data sources or private networks.

Use Case: Moving data from an on-premises database to Azure.
Limitations: Requires installation and maintenance on a local server.

Azure-SSIS IR: Enables running SQL Server Integration Services (SSIS) packages in the cloud.

Use Case: Migrating existing SSIS packages to Azure.
Limitations: Can be more complex to configure.

Q10. How Can You Mask Sensitive Data in the Azure SQL Database? What Are the Different Masking Techniques Available?

Data masking in Azure SQL Database can be achieved using Dynamic Data Masking (DDM), which hides sensitive data in the result set for non-privileged users.

Masking Techniques:

Default Masking: Replaces data with default values (e.g., full masking for sensitive columns).
Email Masking: Masks part of the email address (e.g., example@****.com).
Custom Masking: Allows for custom functions to mask data based on specific requirements.

Q11. What Is Azure Integration Runtime (IR), and How Does It Support Data Movement Across Different Networks?

Azure Integration Runtime (IR) is the compute infrastructure used by Azure Data Factory to provide data integration capabilities across various network environments. It facilitates:

Data movement between cloud and on-premises data stores.
Data transformation through data flows and pipelines.

The self-hosted IR specifically allows secure access to on-premises data sources by establishing a connection to Azure Data Factory through the internet, while Azure IR can directly integrate with cloud sources.

Q12. Explain Slowly Changing Dimension (SCD) Type 1 in a Data Warehouse. How Does It Differ from SCD Type 2?

SCD Type 1 updates existing records with new data, overwriting the old values. It does not maintain historical data, making it simpler but less informative regarding past changes.

SCD Type 2 creates new records for changes, preserving historical data. It adds a start date and an end date to each record to track changes over time.

Differences:

Type 1: No history maintained; only the current state is kept.
Type 2: Full history is maintained, providing a complete view of changes over time.

Q13. SQL Questions on Window Functions – Rolling Sum and Lag/Lead Based. How Do Window Functions Differ from Traditional Aggregate Functions?

Window Functions operate on a set of rows related to the current row, allowing for advanced analytics like running totals or comparisons between rows.

**Rolling Sum:** Calculates the sum of a set of values within a defined window of rows (e.g., the last N rows).
Lag/Lead Functions: Allow you to access data from previous or subsequent rows in the result set without requiring a self-join.

Differences from Aggregate Functions:

Aggregate Functions (like SUM, COUNT) return a single value based on a group of rows, while Window Functions return a value for each row based on a specified range of rows (the window).

Conclusion

Preparing for a data engineering interview at Accenture requires a solid understanding of Azure services, data integration concepts, and database management. Familiarizing yourself with these key questions and their answers will boost your confidence and improve your chances of success. Good luck with your interview!