Accenture Interview Questions, Process, and Tips

SAP ERP is the traditional enterprise resource planning software, while S/4 HANA is the next-generation ERP suite built on the SAP HANA platform.

• SAP ERP is based on traditional on-premise architecture, while S/4 HANA is designed for cloud deployment.

• SAP ERP uses a row-based database structure, while S/4 HANA utilizes an in-memory columnar database for faster data processing.

• SAP ERP requires separate systems for transac...read more

Implementation steps in SAP involve planning, configuration, testing, training, and deployment.

• 1. Planning phase involves defining project scope, objectives, and timeline.

• 2. Configuration phase includes setting up SAP modules according to business requirements.

• 3. Testing phase ensures that the system functions correctly and meets user needs.

• 4. Training phase involves educating users on how to use the SAP system effecti...read more

RICEF is a prioritization framework used in project management to rank tasks based on their impact and effort required.

• RICEF stands for Reach, Impact, Confidence, and Effort.

• Reach refers to the number of people affected by the task.

• Impact is the potential positive outcome of the task.

• Confidence is how sure you are about the estimates for Reach and Impact.

• Effort is the amount of time and resources needed to complete the...read more

Yes, I have worked with APP. The steps involve designing, developing, testing, and deploying the application.

• Design the user interface and functionality of the APP

• Develop the APP using programming languages like Java, Swift, or React Native

• Test the APP for bugs and errors

• Deploy the APP to app stores or internal servers for users to access

Material can be configured by adjusting its properties and parameters to meet specific requirements.

• Identify the specific requirements for the material

• Adjust properties such as density, strength, and flexibility

• Consider parameters like temperature resistance and chemical compatibility

• Test the configured material to ensure it meets the desired specifications

I am responsible for managing client relationships, providing strategic advice, and overseeing project implementation.

• Managing client relationships

• Providing strategic advice

• Overseeing project implementation

Repartition and coalesce are methods used in Apache Spark to manage the number of partitions in a dataset.

• Repartition is used to increase or decrease the number of partitions in a dataset by shuffling the data across the cluster.

• Coalesce is used to decrease the number of partitions in a dataset without shuffling the data, which can improve performance.

• Repartition is more expensive as it involves data shuffling, while c...read more

Hive external tables are tables that reference data files located outside of the Hive warehouse directory.

• External tables do not move the data into the Hive warehouse directory, they only reference it.

• Data files for external tables can be in any format supported by Hive, such as CSV, Parquet, or ORC.

• External tables are useful for scenarios where data needs to be shared between different systems or applications.

• Example:...read more

Spark architecture is a distributed computing framework that provides high-level APIs for processing large datasets.

• Spark architecture consists of a driver program, cluster manager, and worker nodes.

• Driver program is responsible for maintaining the SparkContext and executing the user's application.

• Cluster manager allocates resources across applications in a shared or standalone cluster.

• Worker nodes execute the tasks as...read more

Data skewness refers to imbalance in data distribution, while data shuffling is a technique to randomize data order.

• Data skewness can lead to biased model training and inaccurate predictions.

• Data shuffling is used to prevent model overfitting and improve generalization.

• Example: In a dataset with 90% positive class and 10% negative class, data skewness is present.

• Example: Shuffling data before splitting into training an...read more

Optimizations were done in the projects to improve performance and efficiency.

• Implemented caching mechanisms to reduce database queries and improve response times

• Optimized code by refactoring and removing redundant logic

• Utilized indexing and query optimization techniques to speed up database operations

• Implemented lazy loading to improve loading times for large datasets