MLS-C01 Exploratory Data Analysis Practice Test 6 — 15 Questions

Question 1

A data analyst is using AWS Glue to catalog datasets for exploratory analysis. The analyst wants to understand the schema and data types. Which TWO tools can the analyst use to view the schema of a table in the AWS Glue Data Catalog? (Choose TWO.)

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

Accepted Answer

Amazon Athena. Amazon Athena can query the AWS Glue Data Catalog using SQL, including viewing table schemas via the INFORMATION_SCHEMA or by running DESCRIBE statements on tables. The AWS Glue console directly displays the schema of tables in the Data Catalog under the 'Tables' section. Amazon Redshift query editor is for querying Redshift data warehouses, not for directly viewing Glue Catalog schemas unless a federated query is set up. Amazon QuickSight is a business intelligence tool for visualizing data, not for schema exploration. Amazon S3 console only shows objects in S3 buckets, not the schema of Glue tables.

Answer

Amazon Redshift query editor

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Amazon QuickSight

Answer

Amazon S3 console

Question 2

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

Accepted Answer

Train a Random Forest classifier and use feature_importances_. The most appropriate technique for identifying the most important features for predicting a binary target is to train a Random Forest classifier and use the built-in feature_importances_ attribute (Option C). Random Forest is a supervised ensemble method that provides a ranking of feature importance based on how much each feature reduces impurity (e.g., Gini impurity) across all trees. Option A (t-SNE) is a nonlinear dimensionality reduction technique primarily used for visualization in 2D/3D; it does not provide feature importance. Option B (K-means clustering) is an unsupervised clustering algorithm that does not use the target variable and cannot identify predictive features. Option D (PCA) is an unsupervised dimensionality reduction method that finds principal components maximizing variance, but these components are not directly interpretable as feature importance for a specific target variable.

Answer

Use t-SNE to reduce dimensionality and inspect clusters

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Run K-means clustering and examine cluster centroids

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Apply PCA and select components with highest variance

Question 3

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

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Apply standard scaling to all features

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Use PCA to reduce dimensionality and oversample in principal component space

Question 4

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

Accepted Answer

Box plot of the feature grouped by target class. A box plot displays the distribution of the continuous feature for each category of the binary target, allowing easy comparison of medians, spreads, and outliers. This reveals correlation: if the distributions differ notably between classes, the feature is likely correlated with the target. Option A is wrong because a scatter plot is typically used for two continuous variables, not a binary target. Option B is wrong because a histogram shows the distribution of a single continuous variable, ignoring the target. Option D is wrong because a bar chart of target class counts only shows class frequency, not the relationship with the feature.

Answer

Scatter plot with feature on x-axis and target on y-axis

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Histogram of the feature

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Bar chart of target class counts

Question 5

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

Accepted Answer

Remove the feature from the dataset. When a feature has zero variance, it means all values are identical (constant). Such a feature provides no discriminative information for machine learning models and can be safely removed. Removing it reduces dimensionality without losing any information. Option A is correct. Option B (creating interaction terms) is incorrect because interacting a constant with any feature yields a constant. Option C (min-max scaling) does not change the fact that the feature is constant; it remains constant after scaling. Option D (imputing missing values) is irrelevant since zero variance does not imply missing values.

Answer

Create interaction terms with other features

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Apply Min-Max scaling to normalize the feature

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Impute missing values using the mean

Question 6

A company stores customer transaction data in Amazon S3. A data scientist needs to perform exploratory data analysis using Amazon SageMaker. The dataset is 500 GB in CSV format. Which approach is most cost-effective and time-efficient for initial data profiling?

Accepted Answer

Use Amazon S3 Select to sample rows directly from S3. Amazon S3 Select can query a subset of rows directly from S3 without loading the entire dataset, enabling quick and cost-effective profiling. Option B is incorrect because loading the full 500 GB into a SageMaker notebook instance is expensive and time-consuming. Option C is incorrect because converting to Parquet format adds overhead that is unnecessary for initial profiling. Option D is incorrect because using AWS Glue ETL to transform the entire dataset before analysis is not cost-effective for initial data exploration.

Answer

Load the entire dataset into a SageMaker notebook instance and use pandas

Answer

Convert the data to Parquet format and then use Athena to query

Answer

Use AWS Glue ETL to transform the data and then analyze in Athena

Question 7

A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?

Accepted Answer

Log transformation (natural log). Log transformation is commonly used to reduce right skewness by compressing the range of large values. Option B is wrong because Min-Max scaling only rescales the data to a fixed range and does not alter the distribution shape, so it cannot reduce skewness. Option C is wrong because one-hot encoding is designed for categorical features and does not apply to continuous features. Option D is wrong because a square transformation (power >1) amplifies larger values more than smaller ones, which would increase right skewness rather than reduce it.

Answer

Min-Max scaling

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One-hot encoding

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Square transformation

Question 8

During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?

Accepted Answer

Multicollinearity will make coefficient estimates unstable. A Pearson correlation coefficient of 0.95 indicates strong multicollinearity between the two features. Multicollinearity inflates the variance of coefficient estimates, making them unstable and difficult to interpret. Option A is wrong because redundant information leads to multicollinearity, not underfitting; underfitting occurs when the model is too simple. Option B is wrong because heteroscedasticity refers to non-constant variance of errors, not correlation between features. Option C is wrong because overfitting is more associated with model complexity and variance, not directly with redundant features; in fact, redundant features can cause numerical instability but not necessarily overfitting.

Answer

The model will underfit because of redundant information

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Heteroscedasticity will be introduced

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The model will overfit due to redundant features

Question 9

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. Many tree-based model implementations (e.g., LightGBM, CatBoost) support categorical features natively, handling high cardinality without encoding. Option A is wrong because frequency encoding can introduce target leakage if applied without proper cross-validation. Option C is wrong because label encoding imposes an ordinal relationship that the tree might misinterpret. Option D is wrong because one-hot encoding with 100 categories creates many sparse columns, leading to inefficiency and potential overfitting.

Answer

Replace each category with its frequency in the dataset

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Label encode the feature (assign integers 0-99)

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One-hot encode the feature

Question 10

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

Accepted Answer

Z-score method. Options A and B are correct. Z-score method flags points beyond a threshold (e.g., 3 standard deviations) from the mean. IQR-based outlier detection identifies points beyond 1.5*IQR from the quartiles. Option C is wrong because box plots visualize outliers but are not a detection technique per se; they rely on IQR. Option D is wrong because Pearson correlation is bivariate and not used for univariate outlier detection. Option E is wrong because K-means clustering is typically used for multivariate data and not a standard univariate outlier detection method.

Answer

Box plot visualization

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Pearson correlation coefficient

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K-means clustering

Question 11

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

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Gradient vanishing in neural networks

Question 12

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.. The AWS CLI command `aws s3api list-objects --bucket your-bucket --prefix data/ --query 'Contents[?Size > `1000000`].[Key]'` lists the keys (names) of objects in the bucket under the 'data/' prefix whose size is greater than 1,000,000 bytes. The `--query` uses JMESPath to filter objects where Size > 1000000 and then projects the Key field. Option B is incorrect because it states 'counts', but the command returns keys, not a count. Option C is incorrect because it filters by size, not by date. Option D is incorrect because 1,000,000 bytes is not exactly 1 MB (which is 1,048,576 bytes), so the description 'larger than 1 MB' is inaccurate; the command uses bytes, not MB.

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 13

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.. The stem does not include the IAM policy, so the question cannot be definitively answered as written. However, assuming a typical policy that grants s3:GetObject and s3:PutObject on the training/ prefix and s3:ListBucket with a condition restricting the prefix to training/*, the policy meets the requirements. Therefore, option A is correct under that interpretation. Option B is wrong because s3:DeleteObject is not required for reading and writing. Option C is wrong because s3:GetObject is allowed on the training/ objects, not on the bucket itself. Option D is wrong because the condition on ListBucket is valid and correctly limits listing to the training/ prefix.

Answer

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

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

Answer

No, the condition on ListBucket is invalid.

Question 14

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.. The error message indicates that the training data contains missing (NaN) or infinite values, which causes the loss function to become NaN. This is a common issue when data has not been properly cleaned. Option B is wrong because a high learning rate typically leads to divergence or instability, not NaN values due to data issues. Option C is wrong because an incorrect data format would result in a parsing error, not a NaN loss. Option D is wrong because insufficient memory leads to an out-of-memory error, not NaN values.

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 15

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. 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