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Question 1

You are using Python to analyze data from IoT sensors. You notice that some data points are far off from what you'd expect. What is the most appropriate course of action to ensure data accuracy and reliability?

Options :

A : Consult with domain experts to interpret the outliers

B : Ignore the outliers as they add variance to your dataset

C : Replace the outliers with the median value of the dataset

D : Remove the outliers and proceed with the analysis

Answer: A

Question 2

You are working with a dataset that has a mix of numerical and textual data. Your job is to synthesize this information into a more digestible form. What technique could you use to handle both types of data effectively for downstream analysis?

Options :

A : Run a Principal Component Analysis (PCA) on the entire dataset.

B : Implement a Decision Tree algorithm to identify the most important features.

C : Use k-Nearest Neighbors (k-NN) to classify data points.

D : Utilize Natural Language Processing (NLP) for textual data and Min-Max scaling for numerical data.

Answer: D

Question 3

You are tasked with preparing a dataset that has a mix of continuous, ordinal, and nominal variables for a machine learning model. What would be the most appropriate encoding method for ordinal variables?

Options :

A : Label encoding

B : Binary encoding

C : One-hot encoding

D : Frequency encoding

Answer: A

Question 4

In Python, which of the following methods is not a common statistical approach to identify outliers within a dataset?

Options :

A : Interquartile Range (IQR)

B : Z-score

C : Tukey-s Fences

D : Mean Squared Error (MSE)

Answer: D

Question 5

You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?

Options :

A : df.fillna(value=0)

B : df.replace(to_replace=np.nan, value=0)

C : df['column_name'] = 0

D : df.dropna(subset=['column_name'])

Answer: A