EMC Dell Data Science Foundations D-DS-FN-23 Exam Dumps: Updated Questions & Answers (December 2025)

The Partial Autocorrelation Function (PACF) is examined to identify the order of the autoregressive (AR) component in an ARIMA model. It helps determine how much of the autocorrelation is explained by previous lags, assisting in selecting the appropriate AR order.

The Hadoop Distributed File System (HDFS) is a master/slave system designed to run on commodity hardware, meaning it can efficiently store large volumes of data across inexpensive, off-the-shelf hardware.

Hadoop is primarily written in Java. It is built on the Java programming language to provide a framework for distributed storage and processing of large datasets.

In a user-defined aggregate function (UDAF), FFUNC refers to the optional final calculation function. It is used to perform any final calculation or transformation on the aggregated result before it is returned to the user.

Qualitative data represents non-numeric categories. Nominal data consists of named categories without any order, while ordinal data includes categories with a meaningful order but without consistent intervals between them.

MAD in MADlib stands for Multiple Access Design, which is a library for scalable in-database analytics, including machine learning and statistical methods.

A line chart is specifically designed to display time series data. It shows data points in a sequential order, making it easy to observe trends over time.

Three built-in data types in R are Boolean, integer, and character. These data types are fundamental to R and represent logical values (TRUE/FALSE), whole numbers, and text data, respectively.

A key business driver for Big Data analytics adoption is the ability to identify new business opportunities by analyzing large and complex datasets, which can provide valuable insights for decision-making and strategy development.

The decision tree having 17 layers and a leaf node with only three customers suggests that the tree is overfitting the data. This is because a deep tree with many layers can lead to overfitting, where the model fits the training data too closely, capturing noise and small details that do not generalize well to new data. Reducing the tree depth or pruning the tree can help mitigate overfitting.