Accenture Data Engineer Interview Questions and Answers

Optimisation techniques for improving Databricks performance

• Utilize cluster sizing and autoscaling to match workload demands

• Optimize data storage formats like Parquet for efficient querying

• Use partitioning and indexing to speed up data retrieval

• Leverage caching for frequently accessed data

• Monitor and tune query performance using Databricks SQL Analytics

• Consider using Delta Lake for ACID transactions and improved perfor...read more

Unity catalog is a centralized repository of assets, scripts, and plugins for Unity game development.

• Unity catalog is used by developers to easily access and integrate assets into their Unity projects.

• It includes a wide range of resources such as 3D models, textures, animations, and scripts.

• Developers can search, preview, and download assets from the Unity catalog.

• Unity catalog helps streamline the game development pro...read more

Alerting mechanism in ADF for failed pipelines involves setting up alerts in Azure Monitor and configuring email notifications.

• Set up alerts in Azure Monitor for monitoring pipeline runs

• Configure alert rules to trigger notifications when a pipeline run fails

• Use Azure Logic Apps to send email notifications for failed pipeline runs

Use Python's built-in data structures like sets or dictionaries to handle duplicates.

• Use a set to remove duplicates from a list: unique_list = list(set(original_list))

• Use a dictionary to remove duplicates from a list while preserving order: unique_list = list(dict.fromkeys(original_list))

Use Databricks provided tools like databricks-connect and databricks-cli to migrate Hive metadata to Unity catalog.

• Use databricks-connect to connect to the Databricks workspace from your local development environment.

• Use databricks-cli to export the Hive metadata from the existing Hive metastore.

• Create a new Unity catalog in Databricks and import the exported metadata using databricks-cli.

• Validate the migration by chec...read more

To read a CSV file from an ADLS path, you can use libraries like pandas or pyspark.

• Use pandas library in Python to read a CSV file from ADLS path

• Use pyspark library in Python to read a CSV file from ADLS path

• Ensure you have the necessary permissions to access the ADLS path

Narrow transformation processes one record at a time, while wide transformation processes multiple records at once.

• Narrow transformation processes one record at a time, making it easier to parallelize and optimize.

• Wide transformation processes multiple records at once, which can lead to shuffling and performance issues.

• Examples of narrow transformations include map and filter operations, while examples of wide transfor...read more

Actions and transformations are key concepts in data engineering, involving the manipulation and processing of data.

• Actions are operations that trigger the execution of a data transformation job in a distributed computing environment.

• Transformations are functions that take an input dataset and produce an output dataset, often involving filtering, aggregating, or joining data.

• Examples of actions include 'saveAsTextFile'...read more

Enforcing the schema ensures data consistency and validation, while manually defining the schema in code allows for more flexibility and customization.

• Enforcing the schema ensures that all data conforms to a predefined structure and format, preventing errors and inconsistencies.

• Manually defining the schema in code allows for more flexibility in handling different data types and structures.

• Enforcing the schema can be do...read more

Optimizations like partitioning, caching, and using efficient file formats can reduce overhead in reading large datasets in Spark.

• Partitioning data based on key can reduce the amount of data shuffled during joins and aggregations

• Caching frequently accessed datasets in memory can avoid recomputation

• Using efficient file formats like Parquet or ORC can reduce disk I/O and improve read performance

SQL query to find the name of person who logged in last within each country from Person Table

• Use a subquery to find the max login time for each country

• Join the Person table with the subquery on country and login time to get the name of the person

Rank assigns a unique rank to each row, Dense Rank assigns a unique rank to each distinct row, and Row Number assigns a unique number to each row.

• Rank assigns the same rank to rows with the same value, leaving gaps in the ranking if there are ties.

• Dense Rank assigns a unique rank to each distinct row, leaving no gaps in the ranking.

• Row Number assigns a unique number to each row, without any regard for the values in the...read more

Optimized data processing and storage to enhance performance and reduce latency in ETL workflows.

• Implemented partitioning in our data warehouse to improve query performance, reducing data scan times by 40%.

• Utilized indexing on frequently queried columns, leading to a 30% decrease in query execution time.

• Migrated from a traditional RDBMS to a distributed NoSQL database, which improved scalability and read/write speeds.

• O...read more

Interactive clusters allow for real-time interaction and exploration, while job clusters are used for running batch jobs.

• Interactive clusters are used for real-time data exploration and analysis.

• Job clusters are used for running batch jobs and processing large amounts of data.

• Interactive clusters are typically smaller in size and have shorter lifespans.

• Job clusters are usually larger and more powerful to handle heavy w...read more

To add a column in a dataframe, use the 'withColumn' method. To rename a column, use the 'withColumnRenamed' method.

• To add a column, use the 'withColumn' method with the new column name and the expression to compute the values for that column.

• Example: df.withColumn('new_column', df['existing_column'] * 2)

• To rename a column, use the 'withColumnRenamed' method with the current column name and the new column name.

• Example:...read more

Coalesce is used to combine multiple small partitions into a larger one, while Repartition is used to increase or decrease the number of partitions in a DataFrame.

• Coalesce reduces the number of partitions in a DataFrame by combining small partitions into larger ones.

• Repartition increases or decreases the number of partitions in a DataFrame by shuffling the data across partitions.

• Coalesce is more efficient than Repartit...read more

Union combines and removes duplicates, while union all combines all rows including duplicates.

• Union removes duplicates from the result set

• Union all includes all rows, even duplicates

• Use union when you want to remove duplicates, use union all when duplicates are needed

Facts tables contain numerical data while dimensions tables contain descriptive attributes.

• Facts tables store quantitative data like sales revenue or quantity sold

• Dimensions tables store descriptive attributes like product name or customer details

• Facts tables are typically used for analysis and reporting, while dimensions tables provide context for the facts

Lambda functions in Python are anonymous functions that can have any number of arguments but only one expression.

• Lambda functions are defined using the lambda keyword.

• They are commonly used for small, one-time tasks.

• Lambda functions can be used as arguments to higher-order functions like map, filter, and reduce.