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

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.

When building a K-means clustering model, it's important to ensure that the data is properly scaled, as K-means is sensitive to the scale of the variables. If the variables have different scales, the clustering may not segment as expected. Standardizing or normalizing the data can often improve the results.

Hive converts SQL-like commands into execution plans that run as Tez, Spark, or MapReduce jobs on a Hadoop cluster, enabling users to query large datasets using a familiar query language.

The GROUPING extension in SQL OLAP is used to return a result for each output row, where 1 identifies a summary row and 0 identifies grouped rows. This helps distinguish between regular grouped rows and summary rows in the result set.

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.

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 project goals component of a final presentation provides a succinct overview of the business situation that led to the initiation of the project. It sets the context for why the project was undertaken and what it aimed to achieve.

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

Defining categories such as performance levels falls under the model planning phase, where data is prepared and structured for analysis. This step involves selecting techniques and identifying how data will be used in modeling.

A box and whisker plot is ideal for visualizing the relationship between time spent and product category, especially when you have multiple categories. It shows the distribution of time spent in each product category, including the median, quartiles, and any potential outliers.

A graph of the WSS (Within-Cluster Sum of Squares) versus the value of K helps determine the optimal number of clusters. The "elbow" point on the graph represents the value of K at which adding more clusters no longer significantly improves the WSS.

Feature selection involves identifying the most useful input variables that contribute meaningfully to the model, improving accuracy and reducing complexity during the model building phase.

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.

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.

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.