MLS-C01 Exploratory Data Analysis Practice Test 18 — 15 Questions

Question 1

A data scientist is performing exploratory data analysis on a dataset with both numerical and categorical features. The scientist wants to visualize the pairwise relationships between numerical features and also see the distribution of each feature. Which type of plot should the scientist use?

Accepted Answer

Pair plot (scatter matrix) with histograms on the diagonal.. Option C is correct because a pair plot (scatter matrix) shows pairwise scatter plots and histograms on the diagonal. Option A is wrong because a correlation matrix does not show distributions. Option B is wrong because a heatmap only shows correlation values. Option D is wrong because a box plot does not show pairwise relationships.

Answer

Box plot for each feature.

Answer

Heatmap of the correlation matrix.

Answer

Correlation matrix with numbers.

Question 2

A team is analyzing a dataset with many categorical features that have high cardinality (e.g., ZIP code, user ID). They want to explore relationships between these features and a continuous target variable. Which approach is most appropriate for visualizing these relationships without overwhelming the viewer?

Accepted Answer

Group categories into top K levels and use a box plot for each group.. Option D is correct because aggregating the categories into groups (e.g., top 10 levels) and then using a bar plot or box plot makes the visualization interpretable. Option A is wrong because scatter plots are not suitable for categorical data. Option B is wrong because a heatmap of pairwise chi-square tests is for categorical-categorical relationships, not categorical-continuous. Option C is wrong because a correlation matrix with Pearson correlation is for numerical data.

Answer

Compute a correlation matrix using Pearson correlation.

Answer

Create a scatter plot with each category as a different color.

Answer

Use a heatmap to show pairwise chi-square statistics.

Question 3

A data analyst is investigating a dataset where the target variable is binary (0/1). The analyst wants to check for multicollinearity among the numerical features. Which statistical measure should the analyst use?

Accepted Answer

Variance Inflation Factor (VIF).. Option B is correct because Variance Inflation Factor (VIF) quantifies how much a feature is correlated with other features. Option A is wrong because Pearson correlation only measures pairwise linear relationships, not multicollinearity among multiple features. Option C is wrong because chi-square is for categorical variables. Option D is wrong because mutual information measures dependence but does not specifically detect multicollinearity.

Answer

Mutual information between features and target.

Answer

Chi-square test of independence.

Answer

Pearson correlation coefficient between each pair of features.

Question 4

A data scientist is exploring a dataset with many features and suspects that some features are highly correlated. Which TWO methods can the scientist use to detect and handle multicollinearity before building a linear regression model?

Accepted Answer

Compute Variance Inflation Factor (VIF) for each feature and remove features with VIF > 10.. Options A and C are correct. VIF is a standard measure for detecting multicollinearity; removing features with high VIF reduces multicollinearity. Ridge regression (L2 regularization) can handle multicollinearity by penalizing large coefficients. Option B is wrong because PCA reduces dimensionality but makes models less interpretable and does not directly handle multicollinearity in the original features. Option D is wrong because stepwise selection does not specifically address multicollinearity. Option E is wrong because standardization does not affect collinearity.

Answer

Apply Principal Component Analysis (PCA) and use all components.

Answer

Standardize all features to have zero mean and unit variance.

Answer

Use stepwise feature selection.

Question 5

A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?

Accepted Answer

Add polynomial features (e.g., x^2, x^3) and check if model performance improves.. Options A, B, and C are correct. Scatter plots with a LOESS curve visually reveal non-linearity. Polynomial features allow linear models to capture non-linear relationships. Decision trees can model non-linear interactions without explicit feature engineering. Option D is wrong because correlation measures only linear relationships. Option E is wrong because logistic regression is for binary outcomes.

Answer

Apply logistic regression to binarize the target.

Answer

Compute the Pearson correlation coefficient between the predictor and target.

Question 6

A data analyst is performing exploratory data analysis on a dataset and notices that there are outliers in several numerical columns. Which TWO methods can the analyst use to identify outliers?

Accepted Answer

Calculate z-scores and flag any data points with |z| > 3.. Options B and D are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles. Z-scores identify outliers as points with |z| > 3 (assuming normal distribution). Option A is wrong because mean and median are measures of central tendency, not outlier detection. Option C is wrong because histograms show distribution shape but do not explicitly identify outliers. Option E is wrong because pairwise scatter plots may show outliers but are not a systematic method.

Answer

Create a scatter plot matrix to visually inspect.

Answer

Compare the mean and median of each column.

Answer

Plot a histogram and look for gaps.

Question 7

A data scientist is exploring a dataset stored as a single 2 GB object in S3. The scientist wants to read only a subset of the file (e.g., the first 1000 lines) to perform initial data inspection. Which approach should the scientist take to minimize data transfer and cost?

Accepted Answer

Use S3 Select with a SQL query to retrieve the first 1000 rows.. Option C is correct because S3 Select allows retrieving a subset of data using SQL-like queries, reducing data transfer. Option A is wrong because downloading the entire 2 GB file is inefficient and costly. Option B is wrong because the S3 range read (with Range header) can retrieve bytes, but the user wants lines, not bytes; it's possible but less convenient and still transfers the entire file if lines are not aligned. Option D is wrong because Athena scans the entire file; it's not efficient for a single large file.

Answer

Use the AWS CLI to download the entire file and then use head to get the first lines.

Answer

Use the S3 Range header to read the first 1 MB of the file and parse lines.

Answer

Use Amazon Athena to query the file with LIMIT 1000.

Question 8

A data scientist is trying to list objects in an S3 bucket named 'my-bucket' using the AWS CLI command: `aws s3 ls s3://my-bucket/`. The command fails with an access denied error. The IAM policy attached to the scientist's role is shown in the exhibit. What is the most likely cause of the failure?

Accepted Answer

The condition on the ListBucket action requires all objects to have the tag 'data-type'='training', which may not be satisfied.. Option B is correct because the ListBucket action is conditioned on the s3:ExistingObjectTag/data-type condition, which requires that all objects in the bucket have the tag 'data-type' set to 'training'. However, the ListBucket action applies to the bucket, not individual objects, and the condition might not be evaluated correctly, but more importantly, the condition on ListBucket is unusual; typically conditions on ListBucket should be avoided or use different keys. However, the most direct issue is that the condition must be satisfied for the request, and if the bucket has objects without that tag, the ListBucket action is denied. Option A is wrong because the policy includes s3:ListBucket on the bucket ARN. Option C is wrong because the condition uses StringEquals, not a request parameter. Option D is wrong because the bucket exists and the user has permission if the condition is met.

Answer

The IAM policy does not include the s3:ListBucket action.

Answer

The policy does not grant access to the bucket because it uses 'my-bucket' instead of the full ARN.

Answer

The condition should use 'StringLike' instead of 'StringEquals'.

Question 9

A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?

Accepted Answer

The first query uses a partition column (year), allowing partition pruning, while the second query does not.. Option A is correct because the table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions. Option B is wrong because the data format is text (CSV), not Parquet. Option C is wrong because compression is not mentioned. Option D is wrong because the query is not partitioned correctly; the second query does not use partition columns.

Answer

The data is compressed, and the first query benefits from compression.

Answer

The data is stored in Parquet format, which is optimized for columnar access.

Answer

The second query is not optimized because it uses 'SELECT *'.

Question 10

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler provides built-in data analysis and visualization capabilities, including correlation analysis, making it suitable for this task. Option A (Amazon QuickSight) is a BI tool for dashboards, not embedded data wrangling. Option C (AWS Glue) is for ETL jobs. Option D (Amazon Athena) is for querying data in S3.

Answer

Amazon QuickSight

Answer

Amazon Athena

Answer

AWS Glue DataBrew

Question 11

A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?

Accepted Answer

Use Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.. Using Amazon SageMaker Data Wrangler to generate histograms segmented by class is a straightforward way to visualize feature distributions for each class. Option B (t-tests) may be used later but doesn't provide distribution visualization. Option C (box plots) is a good alternative but not as comprehensive as histograms for distribution shape. Option D (correlation matrix) does not show class-wise distribution.

Answer

Run a t-test for each feature to determine statistical significance between classes.

Answer

Generate box plots for each feature using Amazon QuickSight.

Answer

Compute the correlation matrix between features and the target.

Question 12

A data engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 (10 TB in CSV format). The dataset has 2000 columns and 50 million rows. The engineer needs to compute summary statistics (mean, median, standard deviation) for each numeric column and identify missing values. Which approach is MOST cost-effective and time-efficient?

Accepted Answer

Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.. Using Amazon Athena with columnar formats like Parquet after converting from CSV reduces query costs and improves performance. Option A (SageMaker Data Wrangler) may struggle with 2000 columns. Option B (AWS Glue ETL) is more expensive and slower for simple statistics. Option D (Redshift Spectrum) requires setting up a Redshift cluster, which is overkill.

Answer

Use Amazon Redshift Spectrum to query the data directly from S3.

Answer

Load the data into Amazon SageMaker Data Wrangler and compute statistics interactively.

Answer

Use AWS Glue ETL to compute statistics and write results to S3.

Question 13

A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying to the target variable to improve model performance?

Accepted Answer

Apply log transformation to the target variable.. Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.

Answer

Apply StandardScaler to the target variable.

Answer

Apply MinMaxScaler to the target variable.

Answer

Apply one-hot encoding to the target variable.

Question 14

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 (AWS Glue ETL) requires custom code. Option C (Amazon Athena) can extract parts but not as a feature engineering step. Option D (Amazon EMR) requires more manual effort.

Answer

Amazon Athena

Answer

AWS Glue ETL

Answer

Amazon EMR

Question 15

A data scientist is analyzing clickstream data from a website. The data is stored in Amazon S3 as JSON files, each containing nested arrays. The scientist needs to flatten the nested structures and compute user session durations. Which approach is most efficient for this EDA task?

Accepted Answer

Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration.. AWS Glue DataBrew provides a visual interface to flatten nested JSON and compute derived metrics like session duration without writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option C (EMR with Spark) is more complex. Option D (QuickSight) is visualization only.

Answer

Use Amazon EMR with Apache Spark to process the data.

Answer

Use Amazon Athena with JSON SerDe to query the data and compute session duration with SQL.

Answer

Use Amazon QuickSight to visualize the raw data without flattening.