MLS-C01 Exploratory Data Analysis Practice Test 10 — 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.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Violin plots. Box plots (E) and violin plots (B) are both effective for visualizing the distribution of a continuous variable across different categories. Violin plots combine box plots with kernel density estimation, providing a richer view. Correlation matrix (A) is for numerical variables. Scatter plots (C) require two continuous variables. Bar chart of counts (D) shows frequency distribution of categories, not relationship with a continuous target.

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.. The command `aws s3api list-objects-v2 --bucket <bucket> --prefix logs/2023/ --query 'Contents[?Size > `100000`].[Key,Size]' --output text` lists objects under the specified prefix with a size greater than 100000 bytes (≈100 KB). The output shows three objects, indicating there are three log files larger than 100 KB. Option A is incorrect because the output does not provide exact size ranges (e.g., 150KB-200KB); it only indicates files exceeding 100 KB. Option B is incorrect because the command filters by size, so it does not count all objects in the bucket under the prefix. Option D is incorrect because it states the prefix contains exactly three objects, but the command only returns objects larger than the threshold; there may be additional smaller objects not shown.

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.. The bucket policy requires that all PutObject requests include the x-amz-server-side-encryption header with value 'AES256'. Since the data scientist did not specify any encryption, the request was denied with AccessDenied. Option B is wrong because the error is AccessDenied, not NoSuchBucket. Option C is wrong because the data scientist may have permissions but the condition on encryption is not met. Option D is wrong because region mismatch would give a different error.

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.. The repeated NullPointerException error appears multiple times in the logs, indicating a recurring issue that is most valuable for troubleshooting. Option B is incorrect because the logs show different timestamps, not all the same. Option C is incorrect because connection timeout appears only once, while the NullPointerException is more frequent. Option D is incorrect because the log levels vary, and the majority of logs are not at WARN level.

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). In highly imbalanced datasets (99% negative class), accuracy is misleading because a model that predicts the majority class always achieves 99% accuracy. Precision focuses on false positives and is threshold-dependent. F1 score balances precision and recall but is sensitive to the chosen threshold and may not reflect overall performance. AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds and is robust to class imbalance, making it the most appropriate metric for initial model evaluation.

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