10+ Accenture Data Engineering Analyst Interview Questions and Answers

The problem requires returning an array where each element is the product of all elements in the input array except itself.

• Iterate through the array twice to calculate the product of all elements to the left and right of each element.

• Use two arrays to store the products of elements to the left and right of each element.

• Multiply the corresponding elements from the left and right arrays to get the final product array.

• Handle integer overflow by taking modulo MOD = 10^9 + 7.

• To so...read more

Find the maximum sum of a contiguous subarray within an array of integers.

• Use Kadane's algorithm to find the maximum subarray sum efficiently.

• Initialize two variables: max_sum and current_sum.

• Iterate through the array and update current_sum and max_sum accordingly.

• Return the max_sum as the result.

The number of executors required to load 200 Petabytes of data depends on the size of each executor and the available cache.

• Calculate the size of each executor based on available resources and data size

• Consider the amount of cache available for data processing

• Determine the optimal number of executors based on the above factors

Broadcasting is a technique used in Apache Spark to optimize data transfer by sending smaller data to all nodes in a cluster.

• Broadcasting is used to efficiently distribute read-only data to all nodes in a cluster to avoid unnecessary data shuffling.

• It is commonly used when joining large datasets with smaller lookup tables.

• Broadcast variables are cached in memory and reused across multiple stages of a Spark job.

• The limitation of broadcasting is that it can lead to out-of-memor...read more

To debug a slow block, start by identifying potential bottlenecks, analyzing logs, checking for errors, and profiling the code.

• Identify potential bottlenecks in the code or system that could be causing the slow performance.

• Analyze logs and error messages to pinpoint any issues or exceptions that may be occurring.

• Use profiling tools to analyze the performance of the code and identify areas that need optimization.

• Check for any inefficient algorithms or data structures that coul...read more

Accumulators are used for aggregating values across tasks, while Catalyst optimizer is a query optimizer for Apache Spark.

• Accumulators are variables that are only added to through an associative and commutative operation and can be used to implement counters or sums.

• Catalyst optimizer is a rule-based query optimizer that leverages advanced programming language features to build an extensible query optimizer.

• Catalyst optimizer in Apache Spark optimizes query plans by applying ...read more

Lambda Architecture is a data processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream processing methods. Lambda function is a small anonymous function that can take any number of arguments, but can only have one expression.

• Lambda Architecture combines batch processing and stream processing to handle large amounts of data efficiently.

• Batch layer stores and processes large volumes of data, while speed layer processes r...read more

Airflow is a platform to programmatically author, schedule, and monitor workflows.

• Airflow is written in Python and uses Directed Acyclic Graphs (DAGs) to define workflows.

• It has a web-based UI for visualization and monitoring of workflows.

• Airflow consists of a scheduler, a metadata database, a web server, and an executor.

• Tasks in Airflow are defined as operators, which determine what actually gets executed.

• Example: A DAG can be created to schedule data processing tasks like E...read more

Broadcast Variables are read-only shared variables that are cached on each machine in a Spark cluster rather than being sent with tasks.

• Broadcast Variables are used to efficiently distribute large read-only datasets to all worker nodes in a Spark cluster.

• They are useful for tasks that require the same data to be shared across multiple stages of a job.

• Broadcast Variables are created using the broadcast() method in Spark.

• Example: broadcasting a lookup table to be used in a join...read more

Case classes in Python are classes that are used to create immutable objects for pattern matching and data modeling.

• Case classes are typically used in functional programming to represent data structures.

• They are immutable, meaning their values cannot be changed once they are created.

• Case classes automatically define equality, hash code, and toString methods based on the class constructor arguments.

• They are commonly used in libraries like PySpark for representing structured da...read more

RDD stands for Resilient Distributed Dataset in Spark, which is an immutable distributed collection of objects.

• RDD is the fundamental data structure in Spark, representing a collection of elements that can be operated on in parallel.

• RDDs are fault-tolerant, meaning they can automatically recover from failures.

• RDDs support two types of operations: transformations (creating a new RDD from an existing one) and actions (triggering computation and returning a result).

RDD Lineage is the record of transformations applied to an RDD and the dependencies between RDDs.

• RDD Lineage tracks the sequence of transformations applied to an RDD from its source data.

• It helps in fault tolerance by allowing RDDs to be reconstructed in case of data loss.

• RDD Lineage is used in Spark to optimize the execution plan by eliminating unnecessary computations.

• Example: If an RDD is created from a text file and then filtered, the lineage would include the source file...read more

Prepartition is the process of dividing data into smaller partitions before performing any operations on it.

• Prepartitioning helps in improving query performance by reducing the amount of data that needs to be processed.

• It can also help in distributing data evenly across multiple nodes in a distributed system.

• Examples include partitioning a large dataset based on a specific column like date or region before running analytics queries.