EMC D-DS-FN-23 Dell Data Science Foundations Exam Practice Test

Lift measures how many times more often X and Y occur together than would be expected if they were statistically independent. It is expressed as a ratio, and a lift greater than 1 indicates a positive association between X and Y.

The Scheduler in Hadoop is responsible for allocating compute and memory resources across various applications running on the cluster. It decides how resources are distributed based on policies and availability.

P(Milk∩Eggs) is the probability of both Milk and Eggs being bought together, which is the count for Milk and Eggs (110).

P(Milk)) is the probability of Milk being bought, which is the total for Milk (233) divided by the grand total (374).

P(Eggs) is the probability of Eggs being bought, which is the total for Eggs (150) divided by the grand total (374).

The value of the lift for the association rule Milk -> Eggs is approximately 1.18.

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 naïve Bayes conditional independence assumption is applied to simplify the probability calculations by assuming that the features are independent given the class label. This reduces the complexity of the model and makes computation feasible even with many features.

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.

Using tables of numbers to present all of the data visually should be avoided, as it can overwhelm the audience and make it harder to interpret key insights. Instead, visualizations should simplify data and focus on illustrating trends or patterns effectively.

An Excel file is an example of quasi-structured data because it contains structured data (like rows and columns) but may have irregularities, such as varying data formats, missing headers, or mixed data types within cells, making it less strictly structured than a relational database.

The rug() function in R adds small tick marks along the axes of a plot to represent the distribution of a variable, effectively showing it along both axes for better visual interpretation.

The EXCEPT operator returns rows that exist in the result set of the first SELECT statement but not in the second. It removes duplicates and only shows the difference between the two datasets.

Both matrix and array data structures in R can only contain one data type across all their elements, ensuring consistency in the structure.

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.

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

Regular expressions are highly useful for in-database text analysis, where they can be used to search, match, and manipulate text data efficiently within databases, especially for tasks like pattern matching, data extraction, and validation.

In linear regression, the model output includes coefficients that indicate the relative impact of the input variables on the outcome. These coefficients represent how much each input variable influences the predicted outcome.

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.

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.