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D-DS-FN-23
Dell Data Science Foundations Questions and Answers

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Questions # 1:

What does a branch represent in a decision tree?

Options:

Outcome of a test on a variable

Outcome of all prior decisions

Root of the tree

Class label for the decision tree

Answer

Explanation

A branch in a decision tree represents the outcome of a test or decision on a variable, leading to further splits or classifications based on the test result.

Questions # 2:

In a user-defined aggregate function, what is FFUNC?

Options:

Optional final calculation function

Window function

State transition function

Segment-level calculation function

Answer

Explanation

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.

Questions # 3:

Which visualization technique should be avoided?

Options:

Using a small number of contrasting colors to draw distinctions

Using tables of numbers to present all of the data visually

Achieving a high data-ink ratio

Using visuals to illustrate key points

Answer

Explanation

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.

Questions # 4:

What action occurs during feature selection in the model building phase of the data analytics lifecycle?

Options:

Create new combinations of attributes

Overfit the model to improve prediction accuracy

Identify the most useful input variables

Select a superset of variables to shorten training times

Answer

Explanation

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.

Questions # 5:

In association rules, given items X and Y, what does lift measure?

Options:

Percentage of transactions that contain an itemset with X

Percentage of transactions with Xthat also contain Y

Difference in the probability ofX and Y appearing together compared with expectations as if they were statistically independent

How many times more often X and Y occur together than expected if they were statistically independent, expressed as a ratio

Answer

Explanation

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.

Questions # 6:

Question # 6

Refer to the exhibit.

What is the approximate R-squared value for a linear regression model fitted to the data associated with this scatterplot?

Options:

0.96

0.25

Answer

Explanation

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

Questions # 7:

What is a business driver for Big Data analytics adoption?

Options:

Implement the latest technology and tools

Maintain existing data silos

Identify new business opportunities

Ensure the analysts work in isolation

Answer

Explanation

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.

Questions # 8:

Question # 8

Refer to the exhibit.

To predict whether or not a customer will renew their annual property insurance policy, an insurance company built and operationalized a naïve Bayes classification model. In the model, there are two class labels, renewal and non-renewal, that are assigned to each customer based on their attributes.

A subset of the key attributes, their values, and corresponding conditional probabilities are provided in the exhibit.

A customer has the following attributes:

● Age is greater than 65 years

● Owns their own home

● Renewal month is August

If 20% of customers do not renew the police every year, what is the score for a renewal in the naïve Bayesian model for the customer described above?

Options:

0.0022

0 0027

0.0270

0.0216

Answer

Explanation

The formula for Naïve Bayes is:

For the renewal class, we are given:

● P(Class = Renewal) = 0.8 (since 80% renew the policy)

● P(Age > 65 years | Renewal) = 0.3

● P(Housing = Own | Renewal) = 0.9

● P(Renewal Month = August | Renewal) = 0.1

P(Renewal) = P(Renewal) × P(Age > 65 years | Renewal) × P(Housing = Own | Renewal) × P(Renewal Month = August | Renewal)

P(Renewal) = 0.8 × 0.3 × 0.9 × 0.1 = 0.0216

Questions # 9:

Which SQL OLAP grouping extension returns a result for each output row with 1 identifying a summary row and 0 identifying grouped rows?

Options:

CUBE

GROUPING

GROUP ID

ROLLUP

Answer

Explanation

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.

Questions # 10:

You build a decision tree to classify five different types of customers based on their browsing history from a sample of 500. The resulting decision tree has 17 layers. One of the leaf nodes has only three customers.

What do you conclude?

Options:

The decision tree needs to be rebuilt without the three customers

The decision tree needs to be rebuilt to see if the results change

The sample size is too small, so the classes may not be accurate

Due to large number of layers, there may be an overfitting problem

Answer

Explanation

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.

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