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Meesho PySpark Interview Questions for Data Engineers in 2025

Meesho PySpark Interview Questions for Data Engineers in 2025 Preparing for a PySpark interview? Let’s tackle some commonly asked questions, along with practical answers and insights to ace your next Data Engineering interview at Meesho or any top-tier tech company. 1. Explain how caching and persistence work in PySpark. When would you use cache() versus persist() and what are their performance implications? Answer : Caching : Stores data in memory (default) for faster retrieval. Use cache() when you need to reuse a DataFrame or RDD multiple times in a session without specifying storage levels. Example: python df.cache() df.count() # Triggers caching Persistence : Allows you to specify storage levels (e.g., memory, disk, or a combination). Use persist() when memory is limited, and you want a fallback to disk storage. Example: python from pyspark import StorageLevel df.persist(StorageLevel.MEMORY_AND_DISK) df.count() # Triggers persistence Performance Implications : cache() is ...
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Power BI Developer Interview at Novartis: My Approach to the Questions

Power BI Developer Interview at Novartis: My Approach to the Questions

Excited to share how I would answer the questions asked in a recent interview for a Power BI Developer role at Novartis. These questions cover both technical concepts and practical applications, so let’s dive in!



1️⃣ Introduce Yourself

Answer:
I’m a passionate data professional with [X years] of experience in data visualization, analytics, and reporting. I specialize in Power BI, SQL, and Python, having worked on projects involving dashboard creation, data modeling, and KPI analysis to drive business insights. My experience includes collaborating with cross-functional teams and delivering actionable insights for data-driven decision-making.

2️⃣ Explain Merge and Append Queries

Answer:

  • Merge Queries: Used to join two tables based on a common column (like SQL joins). It’s useful for combining data from different sources.
  • Append Queries: Used to stack or union tables vertically, adding rows from one table to another.

3️⃣ Share 3 Methods to Replace Null Values in Power BI

Answer:

  1. Replace nulls with default values using the Transform Data menu in Power Query.
  2. Use the DAX function IF(ISBLANK(ColumnName), "Default Value", ColumnName) in calculated columns.
  3. Replace nulls during data import by specifying rules in the source query (SQL or Python).

4️⃣ Define Star Schema vs. Snowflake Schema

Answer:

  • Star Schema: A denormalized structure with one fact table and multiple dimension tables directly connected to it. It’s simpler and faster for querying.
  • Snowflake Schema: A normalized structure where dimension tables are further broken into sub-dimensions. It’s more complex but saves storage space.

5️⃣ Discuss Available Data Connections in Power BI

Answer:
Power BI supports various data connections, including:

  • SQL Server, MySQL, PostgreSQL
  • Excel, SharePoint, Google Sheets
  • Azure services (Azure SQL, Azure Data Lake)
  • Cloud connectors like Salesforce, AWS, and more.

6️⃣ Detail Transformations in Your Projects

Answer:
In my projects, I’ve used transformations like:

  • Removing duplicates, splitting columns, and pivoting/unpivoting data in Power Query.
  • Creating calculated columns and measures using DAX for performance metrics.
  • Filtering and merging datasets for better data modeling.

7️⃣ Describe Your Project and Utilized KPIs

Answer:
In a recent project, I created a sales performance dashboard to track regional sales, product trends, and revenue growth. Key KPIs included:

  • Monthly Sales Growth
  • Customer Retention Rate
  • Average Revenue per User (ARPU)

8️⃣ Illustrate Scatter Chart with an Example

Answer:
A scatter chart plots two quantitative measures to analyze correlation.
Example: Visualizing the relationship between Ad Spend and Sales Revenue to identify ROI patterns across campaigns.

9️⃣ Define Fact Table and Dimension Table

Answer:

  • Fact Table: Contains measurable data like sales, revenue, or quantities (e.g., Sales Fact).
  • Dimension Table: Contains descriptive attributes like product names, dates, or customer details (e.g., Product Dimension).

🔟 Differentiate Filters and Slicers in Power BI

Answer:

  • Filters: Apply data restrictions globally or at the report/page/visual level.
  • Slicers: Visual filters users can interact with directly on the dashboard.

1️⃣ Identify Charts Used in Real-Time Projects and Explain

Answer:
Common charts include:

  • Bar/Column Charts: For category-wise comparison (e.g., sales by region).
  • Line Charts: For trends over time (e.g., monthly revenue).
  • Heatmaps: To show intensity variations (e.g., sales by region and time).

2️⃣ Contrast Pie Chart and Donut Chart

Answer:

  • Pie Chart: Circular visualization to show part-to-whole relationships.
  • Donut Chart: Similar but with a hollow center, which allows additional information in the middle.

3️⃣ Elaborate on the Drill Through Concept

Answer:
Drill Through allows users to navigate from a summary view to a detailed view of the data by clicking on specific data points in a Power BI report.

4️⃣ Enumerate Types of Gateways in Power BI

Answer:

  1. On-Premises Data Gateway: For accessing on-premise data.
  2. Personal Gateway: Used by individuals for their datasets.

5️⃣ Define Sync Slicer and Its Application

Answer:
Sync Slicer allows slicers to work across multiple report pages, ensuring consistent filtering.

6️⃣ Explain RLS (Row-Level Security)

Answer:
RLS restricts data access for users based on roles. It is implemented by creating roles in Power BI Desktop and defining DAX filters like Region = "East".

7️⃣ Outline Post-Project Testing Procedures

Answer:

  • Validate data accuracy by cross-checking with source data.
  • Ensure report interactivity (filters, slicers) functions as expected.
  • Conduct performance testing to check load times.

8️⃣ Discuss DAX Functions, Specifically Time Intelligence

Answer:
DAX Time Intelligence functions like TOTALYTD, PREVIOUSMONTH, and DATESMTD help analyze trends over specific timeframes.

9️⃣ Address Many-to-Many Relationships Challenges

Answer:
Challenges include ambiguity and double counting. Resolve by:

  • Using bridge tables for unique key mappings.
  • Redesigning the data model to avoid such relationships.

2️⃣0️⃣ Explain SQL Joins in Detail

Answer:
SQL joins combine data from multiple tables.

  • INNER JOIN: Returns matching records.
  • LEFT JOIN: All records from the left table and matching ones from the right.
  • RIGHT JOIN: Opposite of LEFT JOIN.
  • FULL OUTER JOIN: Combines all records from both tables.

2️⃣1️⃣ Define DDL and DML Commands in SQL

Answer:

  • DDL (Data Definition Language): Commands like CREATE, ALTER, DROP.
  • DML (Data Manipulation Language): Commands like INSERT, UPDATE, DELETE.

2️⃣2️⃣ Contrast Where and Having Clause in SQL

Answer:

  • WHERE: Filters rows before grouping.
  • HAVING: Filters groups after aggregation.

2️⃣3️⃣ Differentiate Left Join and Right Join

Answer:

  • Left Join: Returns all records from the left table and matching records from the right.
  • Right Join: Returns all records from the right table and matching ones from the left.

2️⃣4️⃣ More Insights on Novartis?

Answer:
Novartis is a global healthcare leader focused on innovation. Its data-driven culture emphasizes efficiency, patient care, and groundbreaking research.

2️⃣5️⃣ Any Questions for Us?

Answer:

  • What are the current challenges your team faces with Power BI?
  • How does Novartis plan to expand its data analytics capabilities in the next 5 years?

Conclusion

Preparing for Power BI Developer interviews requires not only technical expertise but also a clear thought process and practical examples. I hope this blog helps you ace your interviews!

Let’s Discuss!
How would you approach these questions? Share your tips in the comments.

Hashtags:
#PowerBI #SQL #DataVisualization #InterviewPreparation #Novartis

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