MLS-C01 Practice Test 27 — 15 Questions

Question 1

During EDA of a dataset for a regression problem, a data scientist notices that the target variable has a right-skewed distribution. Which THREE transformations are appropriate to address this skewness? (Choose THREE.)

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

Accepted Answer

Log transformation. Options A, C, and D are correct. Log transformation, Box-Cox transformation, and Yeo-Johnson transformation are effective methods for reducing right skewness in the target variable. Option B (StandardScaler) standardizes features to have zero mean and unit variance, but does not reduce skewness. Option E (Min-Max scaling) scales features to a fixed range, but does not affect the shape of the distribution.

Answer

StandardScaler (z-score normalization)

Answer

Min-Max scaling

Question 2

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 3

Which THREE are valid reasons to perform feature scaling during exploratory data analysis?

Accepted Answer

To improve performance of distance-based algorithms like KNN.

Answer

To change the shape of the feature distribution.

Answer

To increase the number of features.

Question 4

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 5

Which TWO are appropriate visualizations for exploring the distribution of a single numeric variable? (Select TWO.)

Accepted Answer

Histogram. Options B and E are correct. A histogram displays the frequency distribution of a single numeric variable by binning the data, and a box plot shows the five-number summary (minimum, first quartile, median, third quartile, maximum) to visualize spread and outliers. Option A (heatmap) is typically used for two numeric variables to show density or correlation. Option C (bar chart) is for categorical data, not numeric distribution. Option D (scatter plot) visualizes the relationship between two numeric variables, not the distribution of one.

Answer

Heatmap

Answer

Bar chart

Answer

Scatter plot

Question 6

A data engineer is exploring a dataset with a timestamp column and wants to resample the data to a consistent 1-hour frequency. The data is irregularly spaced. Which approach is most efficient using AWS services?

Accepted Answer

Use Amazon SageMaker Processing with a custom script. Amazon SageMaker Processing jobs allow custom scripts (e.g., using pandas resample) to handle irregular time series, and they are fully managed. Option A is wrong because Amazon EMR with Spark requires cluster management and is more complex for simple resampling. Option B is wrong because AWS Glue with built-in transforms is more suited for batch ETL but may be overkill for this task. Option C is wrong because Amazon Athena with SQL window functions is a query engine and cannot resample easily.

Answer

Use Amazon EMR with Spark

Answer

Use AWS Glue with built-in transforms

Answer

Use Amazon Athena with SQL window functions

Question 7

During exploratory data analysis on a dataset with 1 million rows, a data scientist notices that the distribution of the target variable is highly imbalanced (99% class A, 1% class B). Which technique should be applied to address this imbalance before model training?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class, balancing the dataset. Option A is wrong because random undersampling can discard important data. Option B is wrong because scaling does not address imbalance. Option C is wrong because PCA does not fix imbalance.

Answer

Randomly undersample the majority class to match the minority class size

Answer

Apply standard scaling to all features

Answer

Use PCA to reduce dimensionality and oversample in principal component space

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 machine learning team is analyzing feature importance in a dataset with many categorical features. They plan to use a tree-based model. Which encoding method should they use to handle high-cardinality categorical features without creating too many dummy variables?

Accepted Answer

Target encoding. Target encoding replaces categories with the mean of the target, which is efficient and works well with tree models. Option A is wrong because one-hot encoding creates many columns for high cardinality. Option B is wrong because label encoding imposes ordinality. Option D is wrong because frequency encoding may not capture predictive information.

Answer

One-hot encoding

Answer

Label encoding

Answer

Frequency encoding

Question 10

Which TWO are common steps in exploratory data analysis?

Accepted Answer

Checking for missing values.

Answer

Training a machine learning model.

Answer

Deploying the model to production.

Answer

Tuning hyperparameters.

Question 11

Which TWO techniques are appropriate for detecting outliers in a univariate numeric dataset?

Accepted Answer

Z-score method. Options C and D are correct. The Z-score method identifies outliers by measuring how many standard deviations a data point is from the mean; points with |Z| > 3 are often considered outliers. The Interquartile Range (IQR) method defines outliers as points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. Option A (Cook's distance) is used in regression to identify influential points, not general univariate outlier detection. Option B (Mahalanobis distance) is a multivariate distance measure. Option E (DBSCAN) is a clustering algorithm that can identify outliers in multivariate space, but not specifically for univariate numeric data.

Answer

Cook's distance

Answer

Mahalanobis distance

Answer

DBSCAN clustering

Question 12

A data engineer is performing EDA on a dataset with 1 million rows and 200 columns. The dataset is stored in S3 as CSV files. The engineer notices that some columns have a high proportion of zeros. What is the best approach to determine if these zeros represent missing data or actual zero values?

Accepted Answer

Consult the data source documentation or domain experts to understand the meaning of zero values.. Domain knowledge and documentation are the most reliable ways to understand the meaning of zeros. Option A is wrong because statistical methods cannot distinguish missing vs actual zero without context. Option B is wrong because metadata may not have this detail. Option C is wrong because comparing to other columns might be misleading.

Answer

Check correlation of zero columns with other features; if low, assume zeros are missing.

Answer

Calculate the percentage of zeros and compare with other columns; if unusually high, treat as missing.

Answer

Use AWS Glue Data Catalog to view column statistics and infer missing values.

Question 13

A company uses Amazon SageMaker to train a regression model. After training, the data scientist notices that the training loss decreases but validation loss increases after a few epochs. Which EDA technique could have helped predict this behavior?

Accepted Answer

Plot learning curves showing training and validation loss over epochs. Plotting learning curves, which show training and validation loss over epochs, is the correct EDA technique to detect overfitting. The divergence where training loss decreases but validation loss increases is a clear sign of overfitting. Option A (box plots) helps identify outliers but does not directly indicate overfitting. Option C (residual plots) checks for homoscedasticity in regression, not overfitting. Option D (confusion matrix) is used for classification, not regression.

Answer

Create box plots of each feature to identify outliers

Answer

Generate residual plots to check heteroscedasticity

Answer

Plot confusion matrix on the validation set

Question 14

Which TWO are appropriate techniques for detecting outliers in a dataset during exploratory data analysis?

Accepted Answer

Z-score method (assuming normal distribution)

Answer

One-hot encoding

Answer

Principal component analysis (PCA)

Answer

t-SNE

Question 15

Which TWO of the following are appropriate methods for handling missing data in a dataset?

Accepted Answer

Multiple imputation. Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions. Dropping features with many missing values may lose information.

Answer

Dropping features with more than 50% missing values

Answer

Mean imputation for all features

Answer

Listwise deletion (removing rows with missing values)