You're debugging a piece of code that is returning an array in an unexpected order after a sort() method is applied. What could be a likely cause for this behavior given the default behavior of sort()?
- The array has mixed data types
- The sort() function is asynchronous
- The array elements are all numbers
- The array elements are strings
JavaScript's sort() method by default converts elements to strings and then compares their UTF-16 code units. This means that if the array contains mixed data types, the sorting order might be unexpected. For proper sorting, you should provide a compare function as an argument to sort().
The ______ statement is used to specify a new condition to test if the first condition is false.
- else if
- else
- if else
- switch
The else if statement is used to specify a new condition to test if the first condition in an if statement is false. It allows for branching in code execution based on multiple conditions. It's a fundamental control structure in JavaScript.
Which of the following is NOT a characteristic of closures in JavaScript?
- Data encapsulation and privacy
- Persistence of local variables
- Ability to access outer variables
- Limited use of memory resources
Closures in JavaScript are powerful because they allow functions to remember and access their outer (enclosing) variables even after the outer function has finished executing. This feature can lead to memory usage if not managed properly, but it's not a limitation or disadvantage of closures per se.
Regression imputation can lead to biased estimates if the data is not __________.
- All of the above
- Missing completely at random
- Normally distributed
- Uniformly distributed
Regression imputation can lead to biased estimates if the missingness of the data is not completely at random (MCAR). If there is a systematic pattern in the missingness, regression imputation could lead to bias.
Which plot uses kernel smoothing to give a visual representation of the density of data?
- Box plot
- Histogram
- Kernel Density plot
- Scatter plot
A Kernel Density Plot uses kernel smoothing to give a visual representation of the density of data. It is used for visualizing the Probability Density of a continuous variable. It depicts the probability density at different values in a continuous variable.
Which visualization library in Python is primarily built on Matplotlib and provides a high-level interface for drawing attractive statistical graphics?
- NumPy
- Pandas
- SciPy
- Seaborn
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating attractive graphics and comes with several built-in themes for styling Matplotlib graphics.
Why is it important to deal with outliers before conducting data analysis?
- To clean the data
- To ensure accurate results
- To normalize the data
- To remove irrelevant variables
Dealing with outliers is important before conducting data analysis to ensure accurate results, as outliers can distort the data distribution and statistical parameters.
Suppose you have a model with a high level of precision but low recall. You notice that missing data was handled incorrectly. How might this have affected the model's performance?
- Missing data could have affected the model's complexity.
- Missing data might have introduced false negatives.
- Missing data might have introduced false positives.
- Missing data might have skewed the distribution of the data.
Incorrect handling of missing data may result in the model being trained on a biased dataset, leading to false negatives and subsequently a lower recall.
How does the Min-Max scaling differ from standardization when it comes to handling outliers?
- Both handle outliers in the same way
- Min-Max scaling is more sensitive to outliers than standardization
- Min-Max scaling removes outliers, while standardization doesn't
- Standardization is more sensitive to outliers than Min-Max scaling
Min-Max scaling is more sensitive to outliers than standardization. In Min-Max scaling, if the dataset contains extreme values or outliers, then the majority of the data after scaling could end up within a small interval. On the other hand, standardization does not have a bounding range, which makes it more suitable for handling outliers.
What is Sightly (HTL) in the context of AEM?
- Database Management System
- Design Framework
- Programming Language
- Templating Language
Sightly (HTL) is a templating language in AEM used for creating dynamic and flexible templates for web components.
Which of the following is NOT a deployment option for AEM?
- Cloud Deployment
- Hybrid Deployment
- Mainframe Deployment
- On-Premises Deployment
Mainframe Deployment is not a standard deployment option for AEM. AEM supports deployment in the cloud, on-premises, and hybrid environments.
You are working with a normally distributed data set. How would the standard deviation help you understand the data?
- It can tell you how spread out the data is around the mean
- It can tell you the range of the data
- It can tell you the skewness of the data
- It can tell you where the outliers are
For a normally distributed dataset, the "Standard Deviation" tells you "How spread out the data is around the mean". In a normal distribution, about 68% of values are within 1 standard deviation from the mean, 95% within 2 standard deviations, and 99.7% within 3 standard deviations.