MLS-C01 Exploratory Data Analysis Practice Test 9 — 15 Questions

Question 1

A data scientist is exploring a dataset with categorical variables. Which TWO EDA techniques are appropriate for understanding the relationship between a categorical feature and a continuous target? (Choose TWO.)

Accepted Answer

Violin plots. Options A and C are correct. Box plots show distribution of continuous variable across categories. Violin plots combine box plot and density. Option B is wrong because scatter plot is for two continuous variables. Option D is wrong because bar chart of counts shows frequency, not relationship with target. Option E is wrong because correlation matrix is for numerical features.

Answer

Correlation matrix

Answer

Scatter plot with categorical variable on x-axis

Answer

Bar chart of category counts

Question 2

A data scientist is exploring log files stored in S3. They ran the above AWS CLI command. What does the output indicate about the data, and what EDA step should be taken next?

Accepted Answer

There are 3 log files larger than 100KB in the specified prefix.. Option B is correct because the command filters objects larger than 100000 bytes, and the output shows three large files. Option A is wrong because the command does not count all objects; it filters by size. Option C is wrong because the output shows three files, not all files. Option D is wrong because the command explicitly filters by size, so the output is not all objects.

Answer

All log files are about 150KB-200KB in size.

Answer

There are 3 objects in the bucket under the prefix.

Answer

The prefix 'logs/2023/' contains exactly 3 objects.

Question 3

A data scientist is trying to upload a CSV file to an S3 bucket using the AWS CLI without specifying server-side encryption. The upload fails with an AccessDenied error. Based on the bucket policy exhibit, what is the most likely cause?

Accepted Answer

The upload request did not specify the required server-side encryption.. Option B is correct because the policy requires that all PutObject requests include server-side encryption with AES256. Option A is wrong because the policy allows GetObject and PutObject, but with a condition. Option C is wrong because the condition is on the encryption, not on the bucket. Option D is wrong because the error is AccessDenied, not NoSuchBucket.

Answer

The bucket does not exist.

Answer

The data scientist does not have any permissions to the bucket.

Answer

The data scientist used the wrong AWS region.

Question 4

A data scientist is analyzing application logs in JSON format. Based on the exhibit, which EDA insight is most valuable for troubleshooting?

Accepted Answer

There is a recurring NullPointerException error.. Option B is correct because the repeated NullPointerException suggests a recurring issue that needs immediate attention. Option A is wrong because connection timeout appears only once. Option C is wrong because there are multiple timestamps. Option D is wrong because WARN level appears only once.

Answer

All logs occurred at the same timestamp.

Answer

There is a connection timeout issue.

Answer

Most logs are at WARN level.

Question 5

A data scientist is analyzing a dataset with missing values. Which technique is most appropriate for imputing missing values in a numerical feature that follows a normal distribution?

Accepted Answer

Mean imputation. Mean imputation is suitable for normally distributed data as it preserves the mean. Median is robust to outliers, not normality. Mode is for categorical data. Standard deviation is not an imputation method. KNN imputation is non-parametric.

Answer

Standard deviation imputation

Answer

Mode imputation

Answer

Median imputation

Question 6

A machine learning engineer is exploring a dataset with 50 features. Some features are highly correlated. Which technique should the engineer use to reduce dimensionality while preserving variance?

Accepted Answer

Principal Component Analysis (PCA). PCA (Principal Component Analysis) is the standard technique for dimensionality reduction by projecting data onto principal components that capture maximum variance. LDA is supervised and aims to separate classes. t-SNE is for visualization. Autoencoders can reduce dimensionality but are more complex. Factor analysis assumes latent factors.

Answer

Factor Analysis

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Question 7

A data scientist is analyzing a dataset with a large number of categorical features. The target variable is binary. Which technique should the scientist use to assess the relationship between each categorical feature and the target?

Accepted Answer

Chi-square test of independence. The chi-square test of independence is appropriate for testing association between categorical features and a binary target. ANOVA is for continuous target. Mutual information measures dependency but is not a hypothesis test. Point-biserial correlation is for continuous and binary. Cramér's V is a measure of association after chi-square.

Answer

ANOVA

Answer

Point-biserial correlation

Answer

Cramér's V

Question 8

A data scientist is visualizing the distribution of a numerical feature that is heavily right-skewed. Which visualization technique is most appropriate?

Accepted Answer

Box plot with log scale. A box plot with log scale is effective for skewed data as it shows outliers and distribution shape after transformation. Histogram with log scale also works. KDE is similar to histogram. Q-Q plot checks normality. Scatter plot is for two variables.

Answer

Histogram with linear scale

Answer

Scatter plot

Answer

Q-Q plot

Question 9

A company has a dataset with a timestamp column and multiple numerical metrics. They want to identify seasonality and trends. Which AWS service is best suited for this analysis?

Accepted Answer

Amazon QuickSight. Amazon QuickSight provides built-in time series visualization and forecasting. SageMaker Canvas is for ML models without code. Athena is for querying. CloudWatch is for monitoring AWS resources. Kinesis Data Analytics is for real-time analytics.

Answer

Amazon SageMaker Canvas

Answer

Amazon CloudWatch

Answer

Amazon Athena

Question 10

A data scientist is analyzing a dataset with a binary target variable. The dataset is highly imbalanced (99% negative class). Which metric is most appropriate for evaluating the model's performance during exploratory data analysis?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). The Area Under the ROC Curve (AUC-ROC) is robust to class imbalance and measures the trade-off between true positive rate and false positive rate. Accuracy is misleading. F1 score is sensitive to imbalance but less so for threshold selection. Precision and recall individually are less comprehensive.

Answer

Accuracy

Answer

Precision

Answer

F1 Score

Question 11

A data scientist wants to understand the distribution of a categorical feature with 100 unique values. Which visualization is most appropriate?

Accepted Answer

Bar chart. A bar chart is the most appropriate visualization for displaying the distribution of a categorical feature with 100 unique values because it uses discrete bars to represent the frequency or proportion of each category. Unlike a histogram, which requires continuous numeric bins, a bar chart preserves the distinct categories and allows clear comparison of counts across all 100 levels.

Answer

Histogram

Answer

Scatter plot

Answer

Pie chart

Question 12

A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?

Accepted Answer

Log transformation. Log transformation compresses the range of values and reduces the impact of outliers. Standardization (z-score) does not reduce outlier impact. Min-max scaling is sensitive to outliers. Square root transformation is less effective than log for large outliers. Binning loses information.

Answer

Min-max scaling

Answer

Standardization (z-score)

Answer

Binning

Question 13

A data scientist is performing exploratory data analysis on a dataset with mixed data types (numerical, categorical, text). The goal is to identify clusters of similar records. Which technique is most appropriate?

Accepted Answer

K-prototypes clustering. K-prototypes extends k-means to handle mixed data by combining Euclidean distance for numerical and Hamming distance for categorical. K-means only works with numerical data. DBSCAN works on numerical data. Hierarchical clustering typically uses numerical distance. Gower distance can be used but is less common in clustering algorithms.

Answer

DBSCAN

Answer

Hierarchical clustering

Answer

K-means clustering

Question 14

Which TWO of the following are common techniques for detecting outliers in a numerical feature?

Accepted Answer

Interquartile Range (IQR). Z-score and IQR are standard outlier detection methods. PCA can detect outliers but is not a common direct method. Chi-square is for categorical association. Standard deviation alone is not a method.

Answer

Chi-square test

Answer

Standard deviation

Answer

Principal Component Analysis (PCA)

Question 15

Which THREE of the following are appropriate data visualization techniques for exploring the relationship between two numerical variables?

Accepted Answer

Scatter plot. Scatter plot, hexbin plot, and pair plot are designed for bivariate numerical relationships. Bar chart is for categorical. Box plot is for numerical vs categorical.

Answer

Box plot

Answer

Bar chart