MLS-C01 Practice Exam 59 — 20 Questions

Question 1

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 2

A data scientist is performing exploratory data analysis on a dataset with missing values. The dataset contains a column 'age' with some missing entries. Which technique is most appropriate for imputing missing values in the 'age' column if the data is normally distributed?

Accepted Answer

Replace missing values with the mean of the column.. Option A is correct because mean imputation is appropriate for normally distributed data. Option B is wrong because median imputation is better for skewed data. Option C is wrong because mode imputation is for categorical data. Option D is wrong because dropping rows reduces sample size.

Answer

Drop all rows with missing values.

Answer

Replace missing values with the mode of the column.

Answer

Replace missing values with the median of the column.

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 large number of missing values in several columns. The data scientist wants to impute missing values without introducing bias. Which approach should be used?

Accepted Answer

Use iterative imputation (MICE) to model missing values. Option C is correct because MICE (Multiple Imputation by Chained Equations) is a sophisticated method that models each variable with missing values as a function of other variables, reducing bias. Option A is wrong because mean imputation can reduce variance and bias relationships. Option B is wrong because dropping rows loses data. Option D is wrong because mode imputation for categorical data may introduce bias if missingness is not random.

Answer

Remove rows with any missing values

Answer

Replace missing values with the mode of each column

Answer

Replace missing values with the mean of each column

Question 5

Which TWO of the following are valid reasons to use a sample of the data during exploratory data analysis instead of the full dataset? (Select TWO.)

Accepted Answer

Reduce memory usage and computation time. Options A and D are correct. Sampling reduces memory usage and speeds up interactive analysis. Option B is wrong because sampling can miss rare events. Option C is wrong because model accuracy typically decreases with less data. Option E is wrong because sampling does not remove bias from the original data.

Answer

Remove bias from the original dataset

Answer

Ensure rare events are captured in the analysis

Answer

Improve model accuracy by reducing noise

Question 6

During EDA, a data scientist finds that a feature has a skewed distribution. They want to apply a log transformation to make it more Gaussian-like. Which Amazon SageMaker feature is most appropriate for this transformation?

Accepted Answer

Amazon SageMaker Data Wrangler. Option C is correct because SageMaker Data Wrangler provides a visual interface to apply transformations like log scaling without writing code. Option A is wrong because SageMaker Ground Truth is for labeling, not transformation. Option B is wrong because SageMaker JumpStart is for pre-built models. Option D is wrong because SageMaker Clarify is for bias detection and explainability.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker JumpStart

Answer

Amazon SageMaker Ground Truth

Question 7

A company has a dataset with 1 million rows and 500 features. They want to reduce dimensionality for visualization. Which technique is most suitable for preserving global structure?

Accepted Answer

Principal Component Analysis (PCA). Option A is correct because PCA is a linear technique that preserves global variance. Option B is wrong because t-SNE focuses on local structure. Option C is wrong because LDA requires labels. Option D is wrong because Autoencoders are more complex and not primarily for visualization.

Answer

Autoencoder

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Question 8

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

Accepted Answer

Histogram. Options B and D are correct. Option A is wrong because scatter plot is for two variables. Option C is wrong because heatmap shows correlation. Option E is wrong because bar chart is for categorical data.

Answer

Heatmap

Answer

Bar chart

Answer

Scatter plot

Question 9

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset. The target column is 'price' (continuous). Which EDA analysis would best help decide between linear regression and tree-based models?

Accepted Answer

Check linear relationships between features and target. Option A is correct because checking linearity (e.g., scatter plots of features vs. target) is fundamental for linear model assumptions. Option B is wrong because multicollinearity affects linear regression but not tree models. Option C is wrong because class imbalance is for classification. Option D is wrong because outlier detection is important but not the primary factor for model selection.

Answer

Compute variance inflation factor (VIF) for features

Answer

Detect outliers using Z-score

Answer

Identify class imbalance in the target

Question 10

A machine learning team is building a model to predict customer churn. The dataset has 20 features and 50,000 rows. After initial EDA, they notice that the target variable 'churn' is highly imbalanced (5% churn, 95% non-churn). Which EDA step should the team prioritize to address this imbalance before model training?

Accepted Answer

Analyze the distribution of each feature separately for churn and non-churn groups.. Option D is correct because understanding the distribution of features across churn and non-churn classes helps identify which features drive churn. Option A is wrong because PCA reduces dimensionality but does not address imbalance. Option B is wrong because cross-validation is a modeling step, not EDA. Option C is wrong because removing outliers may worsen imbalance.

Answer

Remove outliers in the majority class to balance the dataset.

Answer

Perform stratified cross-validation to ensure balanced folds.

Answer

Apply Principal Component Analysis (PCA) to reduce noise.

Question 11

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 12

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 13

During exploratory data analysis, a data scientist observes a strong correlation (r=0.95) between two numeric features. The model to be trained is a linear regression. What is the most appropriate action?

Accepted Answer

Remove one of the correlated features.. Option C is correct because high correlation indicates multicollinearity, which can be addressed by removing one feature. Option A is wrong because scaling does not help. Option B is wrong because interaction terms increase multicollinearity. Option D is wrong because regularization helps but is not the first step; removal is simpler.

Answer

Apply standardization to both features.

Answer

Use L2 regularization (Ridge regression) without removing features.

Answer

Create an interaction term between the two features.

Question 14

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. Option D is correct because 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 15

A data scientist runs the above AWS CLI command. What does the command do?

Accepted Answer

It lists objects larger than 1,000,000 bytes under the data/ prefix.. Option B is correct. The command lists objects in the bucket under prefix 'data/' that are greater than 1,000,000 bytes (Size > '1000000') and returns their keys. Option A is wrong because size is in bytes, not 1 MB (1 MB = 1,048,576 bytes). Option C is wrong because it returns keys, not counts. Option D is wrong because it filters by size, not date.

Answer

It counts the number of objects larger than 1 MB.

Answer

It lists objects created after January 2023.

Answer

It lists objects larger than 1 MB in size.

Question 16

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. Option D is correct because 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 Athena is a query engine and cannot resample easily. Option B is wrong because AWS Glue is more suited for batch ETL but may be overkill. Option C is wrong because Amazon EMR requires cluster management and is more complex for simple resampling.

Answer

Use Amazon EMR with Spark

Answer

Use AWS Glue with built-in transforms

Answer

Use Amazon Athena with SQL window functions

Question 17

A data scientist is analyzing a dataset with many categorical features. The target variable is binary. Which statistical test should be used to assess the association between each categorical feature and the target?

Accepted Answer

Chi-squared test of independence. Option C is correct because Chi-squared test is used for independence between categorical variables. Option A is wrong because ANOVA is for continuous target. Option B is wrong because Pearson correlation is for continuous variables. Option D is wrong because Kolmogorov-Smirnov test is for distribution comparison.

Answer

Pearson correlation coefficient

Answer

ANOVA

Answer

Kolmogorov-Smirnov test

Question 18

A data scientist runs the above AWS CLI command and gets the output. The object size is 1 GB. They try to open the CSV file in Amazon Athena but get an error. What is the most likely cause?

Accepted Answer

The file exceeds the maximum CSV file size that Athena can query without partitioning. Option B is correct because Athena has a 100 MB per file limit for CSV queries without partitioning. Option A is wrong because gzip compression is supported. Option C is wrong because 1 GB is large but Athena can handle larger with proper partitioning. Option D is wrong because CSV is supported.

Answer

The file format is not supported by Athena

Answer

The file is not compressed with gzip

Answer

The file is too large for Athena to query at all

Question 19

A machine learning engineer is analyzing a text classification dataset with 50,000 documents. Which EDA step is most important to understand the vocabulary size and frequency distribution?

Accepted Answer

Plot frequency of each word in a bar chart. Option D is correct because word frequency distribution (e.g., zipfian) helps decide vocabulary cutoff. Option A is wrong because TF-IDF is a transformation, not EDA. Option B is wrong because document length distribution is about length, not vocabulary. Option C is wrong because bigram analysis is more advanced; basic frequency is first.

Answer

Compute TF-IDF matrix

Answer

Generate bigram collocations

Answer

Plot histogram of document lengths

Question 20

A data scientist is performing EDA on a dataset with both numerical and categorical features. Which technique is best for detecting multicollinearity among numerical features?

Accepted Answer

Correlation matrix with heatmap. Option B is correct because a correlation matrix quantifies linear relationships between numerical features. Option A is wrong because box plots show distribution, not relationships. Option C is wrong because chi-square test is for categorical associations. Option D is wrong because pair plots visualize scatter plots but not a quantitative measure of multicollinearity.

Answer

Chi-square test of independence

Answer

Box plots for each numerical feature

Answer

Pair plot