Bloom Energy Data Analyst Interview Questions and Answers

Handling difficult stakeholders requires effective communication, active listening, building trust, and managing expectations.

• Establish clear communication channels and set expectations early on

• Actively listen to their concerns and address them in a timely manner

• Build trust through transparency and honesty

• Involve them in decision-making processes to make them feel valued

• Seek common ground and find win-win solutions

• Mana...read more

I have 5 years of experience as a Business Analyst in the finance industry.

• Led cross-functional teams to analyze market trends and develop strategic plans

• Performed data analysis to identify opportunities for cost reduction and revenue growth

• Collaborated with stakeholders to gather requirements and create detailed business requirements documents

Business Analysts use various types of documentation to analyze and communicate business requirements.

• Business requirements documents (BRD)

• Functional requirements documents (FRD)

• Use cases

• User stories

• Process flow diagrams

• Data flow diagrams

• Entity relationship diagrams

• Mockups and wireframes

Spark Dataset is a distributed collection of data organized into named columns.

• It is an extension of the Spark DataFrame API.

• It provides type-safe, object-oriented programming interface.

• It offers better performance and optimization compared to DataFrames.

• Example: val dataset = spark.read.json("path/to/file").as[MyCaseClass]

Spark is faster for real-time processing, while Hadoop is better for batch processing and large-scale data storage.

• Spark is faster than Hadoop due to in-memory processing.

• Hadoop is better for batch processing and large-scale data storage.

• Spark is more suitable for real-time processing and iterative algorithms.

• Hadoop is more suitable for processing large volumes of data in a distributed manner.

• Spark is commonly used for...read more

ADF is a cloud-based data integration service while Synapse is an analytics service that brings together big data and data warehousing.

• ADF is used for data integration and orchestration tasks, while Synapse is used for big data analytics and data warehousing

• ADF allows you to create data pipelines to move and transform data, while Synapse provides a unified experience for data preparation, data management, data warehous...read more

RDD stands for Resilient Distributed Dataset, a fundamental data structure in Apache Spark.

• RDD is a fault-tolerant collection of elements that can be operated on in parallel.

• RDDs are immutable, meaning they cannot be changed once created.

• RDDs support two types of operations: transformations (creating a new RDD from an existing one) and actions (returning a value to the driver program).

SCD1 and SCD2 are types of slowly changing dimensions used in data warehousing.

• SCD1 (Type 1): Overwrites old data with new data, losing historical information.

• SCD2 (Type 2): Keeps track of historical data by creating new records for changes.

• SCD1 is simpler and faster, while SCD2 provides more detailed historical information.

• Example: SCD1 would update a customer's address directly, while SCD2 would create a new record w...read more