MLS-C01 Practice Test 29 — 15 Questions

Question 1

A data scientist loads a large dataset from Amazon S3 into a pandas DataFrame using a SageMaker notebook. The dataset contains a mix of numeric and categorical features. The data scientist wants to quickly check for missing values. Which pandas function is most appropriate?

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

Accepted Answer

df.isnull().sum(). Df.isnull().sum() returns the count of missing values per column. Option A is wrong because df.info() provides column data types and non-null counts, but not missing value counts directly. Option B is wrong because df.describe() only summarizes numeric columns. Option C is wrong because df.shape returns the dimensions, not missing values.

Answer

df.info()

Answer

df.describe()

Answer

df.shape

Question 2

During exploratory data analysis, a data scientist notices that a categorical feature 'city' has over 1,000 unique values. The dataset has 10,000 rows. Which technique should the scientist consider to reduce the cardinality of this feature?

Accepted Answer

Group low-frequency categories into a single 'other' category.. Grouping rare categories into an 'other' bucket is a common technique to reduce cardinality. Option A (label encoding) assigns numeric labels but still has 1000 unique values. Option B (grouping into 'other') reduces cardinality. Option C (one-hot encoding) would create too many columns. Option D (frequency encoding) replaces categories with frequency but still has 1000 values.

Answer

Apply label encoding to assign numeric labels.

Answer

Apply one-hot encoding to all categories.

Answer

Apply frequency encoding to replace each category with its frequency.

Question 3

Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Remove rows or columns with missing values if they are few.. The correct techniques for handling missing values are removing rows/columns with missing values (if the proportion is small) and imputing missing values with statistical measures like the mean or median. Options A (feature scaling), C (PCA), and D (one-hot encoding) are not methods for dealing with missing data; they serve other purposes such as normalization, dimensionality reduction, and encoding categorical variables.

Answer

Apply feature scaling to normalize the data.

Answer

Use Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Apply one-hot encoding to the missing values.

Question 4

Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)

Accepted Answer

Impute with the mode (most frequent) of the column.. Options B and C are correct. Imputing with the mode (B) is a simple and effective method for categorical data, as it preserves the most frequent category without introducing new values. Treating missing values as a separate 'Unknown' category (C) allows the model to capture potential patterns associated with missingness, which can be informative. Option A is incorrect because one-hot encoding is a technique for representing categorical variables, not for handling missing data; it requires the values to be known first. Option D is incorrect because dropping rows with missing values can result in significant data loss and may introduce bias, especially if missingness is not random. Option E is incorrect because mean imputation is suitable for numerical data, not categorical data.

Answer

Use one-hot encoding to represent missingness as a binary feature.

Answer

Drop all rows with missing values in that column.

Answer

Impute missing values with the mean of the column.

Question 5

A data scientist is performing EDA on a time series dataset of daily website visits. The scientist wants to identify any seasonality patterns. Which visualization is most appropriate?

Accepted Answer

Line plot with day on x-axis and visits on y-axis, highlighting weekends.. A line plot with day on the x-axis and visits on the y-axis, with weekends highlighted, can reveal weekly seasonality patterns. Option A (correlation matrix with lags) can detect autocorrelation but is not a direct visualization of seasonality. Option B (scatter plot vs day of month) could show monthly patterns but is less effective for daily seasonality and does not preserve time order as clearly as a line plot. Option C (histogram) shows distribution, not temporal patterns. Therefore, option D is best.

Answer

Correlation matrix of visits with lagged versions of itself.

Answer

Scatter plot of visits against the day of the month.

Answer

Histogram of daily visit counts.

Question 6

Refer to the exhibit. A data scientist lists files in an S3 bucket. The dataset is split into train, test, and validation sets. What is the most likely issue with this data split?

Accepted Answer

The training set is smaller than the test set, which is unusual.. The training set (1024 bytes) is smaller than the test set (2048 bytes), which is unusual. Typically training set should be larger. Option A (missing header) cannot be inferred; Option B (CSV format) is fine; Option D (partitioning) is not evident.

Answer

The files are not partitioned by date.

Answer

The training file is missing a header row.

Answer

The test file should be in JSON format.

Question 7

A data scientist is working with a dataset containing customer transactions. The dataset has a column named 'transaction_date' with timestamp values. The scientist wants to create new features such as day of week, hour, and whether the transaction occurred on a weekend. Which AWS service provides built-in feature engineering capabilities for datetime columns?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler includes built-in transformations for datetime features like extracting day, month, hour, etc. Option B (Amazon Athena) can extract parts but not as a feature engineering step. Option C (AWS Glue ETL) requires custom code. Option D (Amazon EMR) requires more manual effort.

Answer

Amazon Athena

Answer

AWS Glue ETL

Answer

Amazon EMR

Question 8

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target is binary. The scientist wants to reduce dimensionality while preserving information related to the target. Which TWO methods are appropriate?

Accepted Answer

L1-regularized logistic regression. Options C and D are correct. L1-regularized logistic regression (option C) drives coefficients to zero for irrelevant features, effectively performing feature selection. Mutual information-based feature selection (option D) measures dependency between each feature and the target, selecting features with highest mutual information. Option A (PCA) is unsupervised and may discard target-related variance. Option B (Autoencoders) is unsupervised and not directly target-aware. Option E (t-SNE) is for visualization, not feature selection.

Answer

Principal Component Analysis (PCA)

Answer

Autoencoders

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Question 9

A data scientist is exploring a dataset with many features and wants to detect multicollinearity. Which technique should the scientist use?

Accepted Answer

Calculate the Variance Inflation Factor (VIF) for each feature.. Variance Inflation Factor (VIF) is a standard metric for detecting multicollinearity. Option D (pairwise scatter plots) can hint at relationships but does not quantify multicollinearity. Option B (Pearson correlation matrix) shows pairwise linear correlation but does not capture multicollinearity among multiple variables. Option C (ANOVA) is used for comparing means, not for detecting multicollinearity.

Answer

Compute the Pearson correlation matrix between features.

Answer

Perform ANOVA on each feature against the target.

Answer

Create pairwise scatter plots of all features.

Question 10

Which TWO actions are appropriate when handling missing data in a dataset for machine learning? (Select TWO.)

Accepted Answer

Use a machine learning model to predict missing values based on other features. Options A and C are correct. Using a machine learning model to predict missing values is a valid imputation technique that can preserve relationships in the data. Imputing with the mean or median is a standard approach for numerical features and maintains the dataset size. Option B is incorrect because dropping all rows with any missing values can lead to significant data loss, especially if missingness is widespread. Option D is incorrect because removing an entire feature due to missing values might discard predictive information unless the feature is mostly missing. Option E is incorrect because filling all missing values with zero can introduce bias and distort distributions, as zero may not be a natural placeholder for the data.

Answer

Drop all rows that contain any missing value

Answer

Remove the feature entirely if it contains missing values

Answer

Fill missing values with zero

Question 11

A data scientist uses SageMaker Studio to run EDA on a dataset with 500 features. The goal is to reduce dimensionality before modeling. Which EDA technique should the data scientist use to understand the variance explained by each feature?

Accepted Answer

Scree plot of principal components. A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a histogram shows distribution, not variance. Option C is wrong because a heatmap of correlations shows pairwise relationships, not variance. Option D is wrong because a box plot shows summary statistics.

Answer

Histogram of the target variable

Answer

Heatmap of feature correlations

Answer

Box plot of each feature

Question 12

A data scientist is analyzing a dataset with missing values. Which THREE methods are appropriate for handling missing data during EDA and preprocessing?

Accepted Answer

Remove rows with any missing values. (remove rows with any missing values) is appropriate if missing data is random and limited. Option B (impute with mean) is commonly used for numeric features without outliers. Option E (impute with median) is robust to outliers. Option C (replace missing values with 0) is generally not recommended as it can introduce bias unless 0 is a valid value. Option D (ignore missing values and proceed with modeling) is problematic because most algorithms cannot handle missing values and will raise errors.

Answer

Replace missing values with 0

Answer

Ignore missing values and proceed with modeling

Question 13

A data scientist is performing EDA on a dataset with 1,000 features. The goal is to select the most important features for a regression model. Which technique can be used to rank feature importance quickly?

Accepted Answer

Calculate the correlation coefficient of each feature with the target. Correlation analysis with the target variable is a quick way to rank features. Option B (t-SNE) is used for visualization, not feature ranking. Option C (k-means clustering) is an unsupervised clustering method and does not provide feature importance. Option D (PCA) component loadings show variance contribution but are not a direct ranking of feature importance to the target.

Answer

Use t-SNE to visualize feature relationships

Answer

Run k-means clustering and use cluster centroids

Answer

Apply Principal Component Analysis (PCA) and examine component loadings

Question 14

A data scientist is working with a dataset that has imbalanced classes (1% positive). They want to explore the data before modeling. Which visualization technique is most appropriate to understand the distribution of features with respect to the target class?

Accepted Answer

Parallel coordinates plot. Parallel coordinates plot can show feature patterns for minority vs majority class in high dimensions. Option A is wrong because box plots are univariate and do not show interactions between features. Option C is wrong because histograms are univariate and do not show interaction. Option D is wrong because scatter plot matrices become cluttered with many features.

Answer

Box plots grouped by class

Answer

Histograms overlaid by class

Answer

Scatter plot matrix

Question 15

A data scientist wants to identify outliers in a dataset. Which TWO techniques are commonly used for outlier detection during EDA?

Accepted Answer

Box plot. Box plots (A) visually identify outliers as points beyond the whiskers (typically 1.5×IQR). Z-score analysis (C) flags data points with an absolute Z-score greater than 3, indicating they are far from the mean. Heatmaps (B) show correlations between variables, not outliers. Pearson correlation (E) measures linear relationships, not outliers. Bar charts (D) display categorical frequencies and do not detect outliers.

Answer

Heatmap

Answer

Bar chart

Answer

Pearson correlation coefficient