Dell Data Science Foundations Practice Questions

This approach directly addresses both project goals. Association Rules are the ideal method for the primary requirement of finding 'items frequently purchased together' (market basket analysis). K-means clustering is a standard and scalable method for the secondary goal of segmenting users into profiles based on their behavior. Both methods are well-suited for implementation on a Big Data platform like Hadoop/Spark.

The significance level, denoted by alpha (α), is the probability of rejecting the null hypothesis when it is actually true. This is the definition of a Type I error. By setting alpha (commonly to 0.05 or 0.01), the researcher establishes the maximum acceptable risk for incorrectly concluding that an effect exists when it does not.

Pruning a decision tree is a classic example of managing the bias-variance trade-off. A large, complex tree (unpruned) has low bias but high variance; it fits the training data perfectly but generalizes poorly to new data (overfitting). Pruning simplifies the tree by removing branches, which increases its bias (it may not fit the training data as well) but decreases its variance, leading to better performance on unseen data.

The core advantage of in-database analytics is that it brings the analytical processing to the data, rather than moving large volumes of data to a separate analytics server. This significantly reduces the time, network bandwidth, and cost associated with ETL (Extract, Transform, Load) processes, leading to faster insights and improved data security since the data never leaves the database environment.

Although zip codes are numbers, they do not represent a quantity. The difference between zip code 90210 and 90211 is not a meaningful value of 1. There is no inherent magnitude or rank order that is useful for most models. Therefore, it should be treated as a nominal categorical variable, where each zip code is a distinct category. This often requires encoding techniques like one-hot encoding before being used in a model.

Using a regression model (or another predictive imputation method) is the most robust approach. It leverages relationships with other variables to estimate the missing values, preserving the data's underlying structure better than simple mean/median imputation. Deleting 15% of the data would cause significant information loss. Replacing a date with the mean is statistically inappropriate for temporal data and could distort the distribution.

Lift measures how much more likely two items are to be purchased together than would be expected if they were statistically independent. A lift of 3.5 means the presence of diapers in a transaction makes the purchase of beer 3.5 times more likely than it would be in a random transaction. It quantifies the strength of the association beyond random chance.

TF-IDF is a numerical statistic that reflects how important a word is to a document in a collection or corpus. It increases the weight of terms that appear frequently in a document (high Term Frequency) but penalizes terms that appear in many documents (low Inverse Document Frequency). This helps highlight terms that are characteristic of a specific document, making them more useful for classification.

MapReduce is designed for large-scale data processing like this. For a multi-stage workflow, Oozie is the standard Hadoop workflow scheduler. It allows you to define a Directed Acyclic Graph (DAG) of actions, chaining multiple MapReduce jobs (or other actions like Pig or Hive scripts) together, handling dependencies and failures. Flume is for data ingestion, Sqoop for database transfer, and Zookeeper for coordination, none of which manage complex processing workflows.

This statement is false. 'Veracity' refers to the uncertainty, quality, and trustworthiness of the data. The speed at which data is generated and processed is referred to as 'Velocity'.