D-DS-FN-23 EMC Dell Data Science Foundations Free Practice Exam Questions (2025 Updated)

Data conditioning in the data preparation phase refers to the process of cleaning the data, normalizing datasets, and performing transformations to ensure the data is in a suitable format for analysis or modeling.

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.

MapReduce is designed to process a few large files that are split into blocks and then processed in parallel across multiple machines. This approach allows for efficient distributed processing of large datasets.

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.

A logarithmic transformation is commonly used to reduce right skewness (long tail to the right) and make data more normally distributed.

The metrics used to calculate relevance in text analysis are TF (Term Frequency) and IDF (Inverse Document Frequency). TF measures how frequently a term appears in a document, while IDF adjusts the importance of the term based on how common it is across all documents. Their combination, known as TF-IDF, helps evaluate the relevance of terms in a given document relative to a larger corpus.

Linear regression is the appropriate technique for estimating a continuous target variable, such as home sale price, based on other continuous and categorical input variables like square footage, number of bedrooms, and lot size.

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.

Spark's in-memory data processing avoids the need to write intermediate data to disk, which significantly speeds up processing compared to MapReduce, which writes intermediate data to disk after each operation. This makes Spark much faster for iterative tasks.

The R-squared value is approximately 0.96, indicating that 96% of the variance in the data is explained by the model.

Ambari, Oozie, and Zookeeper are tools used for cluster and workflow management in Hadoop. Ambari manages and monitors clusters, Oozie handles workflow scheduling, and Zookeeper coordinates distributed processes.

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.

A Type I error occurs when the null hypothesis is rejected even though it is actually true. This is also known as a "false positive" in hypothesis testing.

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.

Hypothesis testing is the appropriate approach to determine whether a significant shift in spending has occurred. By comparing pre- and post-program spending, hypothesis testing can assess if the observed changes are statistically significant.

In ANOVA (Analysis of Variance), the null hypothesis is that all population means are equal to each other. The goal is to test whether there is evidence to reject this hypothesis, indicating that at least one population mean differs from the others.