MLS-C01 Exploratory Data Analysis Practice Test 5 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset of customer churn. The dataset includes a categorical feature 'Region' with 100 unique values. What is the best way to encode this feature for a tree-based model?

Accepted Answer

Use the feature as a categorical variable directly in the tree-based model. Option C is correct because tree-based models can handle high-cardinality categorical features natively without encoding; many implementations (e.g., LightGBM, CatBoost) support categorical features directly. Option A is wrong because one-hot encoding creates 100 columns, causing sparsity. Option B is wrong because label encoding imposes ordinality. Option D is wrong because frequency encoding may cause target leakage if using target encoding without proper cross-validation.

Answer

Replace each category with its frequency in the dataset

Answer

Label encode the feature (assign integers 0-99)

Answer

One-hot encode the feature

Question 2

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous dataset? (Select TWO.)

Accepted Answer

Z-score method. Options B and D are correct. IQR-based outlier detection identifies points beyond 1.5*IQR from quartiles. Z-score method flags points beyond a threshold (e.g., 3) from mean. Option A is wrong because clustering is multivariate. Option C is wrong because box plots visualize outliers but are not a detection technique per se; they use IQR. Option E is wrong because correlation is bivariate.

Answer

Box plot visualization

Answer

Pearson correlation coefficient

Answer

K-means clustering

Question 3

Which THREE of the following are common issues that can be identified during exploratory data analysis? (Select THREE.)

Accepted Answer

Multicollinearity between features. Multicollinearity occurs when two or more features in a dataset are highly correlated, meaning they contain redundant information. During exploratory data analysis (EDA), correlation matrices and variance inflation factor (VIF) calculations can reveal this issue, which can destabilize linear regression models and inflate coefficient standard errors.

Answer

High latency in API endpoints

Answer

Gradient vanishing in neural networks

Question 4

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 5

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 6

A data scientist is setting up an IAM policy for a SageMaker notebook instance that needs to read and write data in the 'training/' folder of an S3 bucket, and also list objects in the bucket. Does the policy satisfy the requirements?

Accepted Answer

Yes, the policy correctly grants the required permissions.. Option A is correct. The policy allows GetObject and PutObject on the training/ folder, and ListBucket with a condition restricting prefix to training/*, which allows listing only that prefix. Option B is wrong because the policy works. Option C is wrong because the condition is valid. Option D is wrong because the policy is sufficient.

Answer

No, the policy must also include s3:DeleteObject for data cleaning.

Answer

No, the policy misses s3:GetObject for the bucket itself.

Answer

No, the condition on ListBucket is invalid.

Question 7

A data scientist receives the above error during model training. What is the most likely cause?

Accepted Answer

The training data contains missing or infinite values.. Option B is correct. The error indicates NaN or infinite values in the input data. Option A is wrong because the error is about data, not hyperparameters. Option C is wrong because the error is not about memory. Option D is wrong because the error is not about data format.

Answer

The learning rate is too high.

Answer

The data format is incorrect; expected CSV but received JSON.

Answer

The instance type lacks sufficient memory.

Question 8

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?

Accepted Answer

AWS Glue. Option C is correct because AWS Glue provides built-in transformations to parse timestamps and extract date/time features. Option A is wrong because Amazon Athena is a query service, not a transformation service. Option B is wrong because Amazon SageMaker Studio is an IDE, not a data transformation service. Option D is wrong because AWS Data Pipeline is a workflow orchestration service, not a timestamp parsing tool.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Data Pipeline

Question 9

A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited for this task?

Accepted Answer

Amazon SageMaker Processing. Option D is correct because Amazon SageMaker Processing jobs can run custom scripts that can handle high cardinality categorical features using libraries like pandas. Option A is wrong because SageMaker Data Wrangler is a visual tool, but it may not be as flexible for custom grouping logic. Option B is wrong because SageMaker Autopilot is for automated ML, not for custom data processing. Option C is wrong because AWS Glue Studio is a visual ETL tool, but it may not be as tightly integrated with SageMaker.

Answer

Amazon SageMaker Data Wrangler

Answer

AWS Glue Studio

Answer

Amazon SageMaker Autopilot

Question 10

A data analyst needs to visualize the distribution of a numerical feature in a dataset. Which AWS service can be used to create a histogram directly from data stored in S3 without writing code?

Accepted Answer

Amazon QuickSight. Option B is correct because Amazon QuickSight is a BI service that can connect to S3 data and create histograms without coding. Option A is wrong because Amazon SageMaker Studio requires code or notebook. Option C is wrong because Amazon Athena outputs query results, not visualizations directly. Option D is wrong because AWS Glue is for ETL, not visualization.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Glue

Question 11

A team is exploring a dataset with missing values in multiple columns. They want to decide whether to drop rows or impute values. Which approach is most appropriate for exploratory data analysis?

Accepted Answer

Analyze the missing data pattern using visualizations and summary statistics. Option A is correct because during EDA, it is important to first understand the pattern and extent of missing data before deciding on treatment. Option B is wrong because dropping rows without analysis may discard valuable data. Option C is wrong because imputing without understanding the missing mechanism may introduce bias. Option D is wrong because EDA does not require using a specific AWS service.

Answer

Impute missing values with the mean of each column

Answer

Drop all rows with missing values to ensure data quality

Answer

Use Amazon SageMaker Data Wrangler to automatically impute missing values

Question 12

A data scientist is performing EDA on a large dataset (10 TB) stored in S3. They need to compute summary statistics for each column. Which approach is most cost-effective and efficient?

Accepted Answer

Use Amazon Athena with SQL queries. Option D is correct because Amazon Athena uses a serverless query engine that scales automatically and charges per query based on data scanned, making it cost-effective for large datasets. Option A is wrong because downloading to SageMaker Studio may incur high data transfer costs and require significant local storage. Option B is wrong because AWS Glue Spark jobs have overhead and are more suited for complex ETL. Option C is wrong because Amazon EMR requires provisioning clusters and is more expensive for simple statistics.

Answer

Use an AWS Glue ETL job with PySpark to compute statistics

Answer

Download the dataset to an Amazon SageMaker Studio notebook and use pandas

Answer

Launch an Amazon EMR cluster and use Spark SQL

Question 13

A company has customer feedback data stored in CSV files in S3. The data includes a 'feedback_text' column. Which AWS service is best suited for performing sentiment analysis as part of exploratory data analysis?

Accepted Answer

Amazon Comprehend. Option A is correct because Amazon Comprehend is a natural language processing (NLP) service that can perform sentiment analysis directly. Option B is wrong because Amazon Lex is for conversational interfaces, not text analysis. Option C is wrong because Amazon Rekognition is for image and video analysis. Option D is wrong because Amazon Textract is for extracting text from documents, not sentiment.

Answer

Amazon Rekognition

Answer

Amazon Textract

Answer

Amazon Lex

Question 14

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 15

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