MLS-C01 Exploratory Data Analysis Practice Test 7 — 15 Questions

Question 1

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?

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

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing allows you to run custom data processing scripts (e.g., using pandas) that can handle grouping rare categories into 'Other' based on frequency thresholds. Option B (Data Wrangler) is a visual tool that may not offer the same level of customization for complex grouping logic. Option C (AWS Glue Studio) is a visual ETL tool but lacks tight integration with SageMaker and may be less efficient for this specific task. Option D (Autopilot) is designed for automated model building, not custom data processing.

Answer

Amazon SageMaker Data Wrangler

Answer

AWS Glue Studio

Answer

Amazon SageMaker Autopilot

Question 2

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. Mazon QuickSight (Option C) because it is a business intelligence (BI) service that can connect directly to data stored in Amazon S3 and create visualizations such as histograms without requiring any coding. Amazon Athena (Option A) is an interactive query service that returns raw query results, not visualizations. Amazon SageMaker Studio (Option B) is a machine learning IDE that typically requires writing code or using notebooks to generate plots. AWS Glue (Option D) is a serverless data integration service for ETL operations, not for visualization.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Glue

Question 3

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. During EDA, the first step is to understand the pattern and extent of missing data using visualizations and summary statistics. This helps determine whether missingness is random or systematic, and guides the choice of imputation or deletion. Option A is wrong because imputing with the mean without understanding the missing mechanism can introduce bias. Option C is wrong because dropping rows may discard valuable data and reduce sample size unnecessarily. Option D is wrong because using SageMaker Data Wrangler is a specific tool and may not be necessary; EDA focuses on understanding data, not automated imputation.

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 4

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. Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in S3, charging only for the data scanned per query. This makes it highly cost-effective and efficient for computing summary statistics on large datasets without needing to provision or manage infrastructure. Option A is wrong because AWS Glue ETL jobs with PySpark have startup overhead and are better suited for complex data transformation tasks rather than simple ad-hoc analysis. Option C is wrong because downloading 10 TB to a SageMaker Studio notebook incurs high data transfer costs and requires substantial local storage, which is neither efficient nor cost-effective. Option D is wrong because launching an Amazon EMR cluster involves provisioning and managing compute resources, leading to higher costs and complexity for a straightforward statistical analysis that Athena can handle more simply.

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 5

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. 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 EMR with Spark requires cluster management and is more complex for simple resampling. Option B is wrong because AWS Glue with built-in transforms is more suited for batch ETL but may be overkill for this task. Option C is wrong because Amazon Athena with SQL window functions is a query engine and cannot resample easily.

Answer

Use Amazon EMR with Spark

Answer

Use AWS Glue with built-in transforms

Answer

Use Amazon Athena with SQL window functions

Question 6

A data analyst wants to check for duplicate rows in a dataset stored in S3. Which AWS service can be used to run a SQL query to count duplicates without moving the data?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows running standard SQL queries directly on data stored in Amazon S3, without needing to move the data. It can easily count duplicate rows using SQL GROUP BY and HAVING clauses. Option B (Amazon Redshift Spectrum) is wrong because although it can query data in S3, it requires an active Redshift cluster, which is unnecessary for this simple ad-hoc query. Option C (AWS Glue) is wrong because Glue is an ETL service for data preparation and cataloging, not a query engine. Option D (Amazon SageMaker Studio) is wrong because it is an integrated development environment for machine learning, not a SQL query service.

Answer

Amazon Redshift Spectrum

Answer

Amazon SageMaker Studio

Answer

AWS Glue

Question 7

Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)

Accepted Answer

Impute missing values using mean or median for numerical features. Options C and D are correct. Imputing missing values using mean or median for numerical features (C) is a common technique during EDA to preserve data size. Visualizing the missing data pattern with heatmaps or bar charts (D) helps understand the distribution and mechanism of missingness. Option A is incorrect because ignoring missing values can introduce bias and lead to inaccurate models. Option B is incorrect because replacing with -1 may distort the data distribution and is not a standard practice. Option E is incorrect because deleting all rows with missing values can cause significant data loss, especially if missingness is not random.

Answer

Ignore missing values and proceed with modeling

Answer

Replace missing values with -1 to indicate missing

Answer

Delete all rows with any missing values

Question 8

Which THREE of the following are best practices for feature engineering during EDA? (Select THREE.)

Accepted Answer

Apply log transformation to highly skewed features. Applying a log transformation to highly skewed features helps normalize their distribution, reducing the impact of extreme values and making the data more suitable for many machine learning algorithms that assume normally distributed features. This is a common technique during exploratory data analysis (EDA) to stabilize variance and improve model performance, especially for linear models and neural networks.

Answer

Remove all outliers from the dataset

Answer

Standardize all features to have zero mean and unit variance

Question 9

Which TWO AWS services can be used to visualize data distributions as part of exploratory data analysis? (Select TWO.)

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a cloud-native business intelligence service that can visualize data distributions through histograms, box plots, scatter plots, and other chart types, making it suitable for exploratory data analysis. Amazon SageMaker Data Wrangler provides a visual interface to create data distribution charts (e.g., histograms, bar charts) directly within the data preparation workflow, enabling quick inspection of feature distributions before model building.

Answer

AWS Glue

Answer

Amazon Athena

Answer

Amazon Comprehend

Question 10

An Athena query SELECT COUNT(*) FROM table WHERE col1 IS NULL returns the value 5000. What does this value represent?

Accepted Answer

The number of rows where col1 is NULL. The exhibit shows the result of an Athena query that counts the number of rows where col1 is NULL. The value 5000 is that count. Therefore, Option B is correct. Option A is incorrect because the query does not count total rows; it filters for NULLs. Option C is incorrect because the query counts NULL rows, not non-NULL. Option D is incorrect because the query does not use DISTINCT to count distinct values.

Answer

The total number of rows in the table

Answer

The number of rows where col1 is not NULL

Answer

The number of distinct values in col1

Question 11

The exhibit shows an IAM policy for a SageMaker notebook. A data scientist wants to use the notebook to run an Athena query and then load the results into a pandas DataFrame. Which action is NOT possible with this policy?

Accepted Answer

Read the Athena query results from the output S3 location. The policy grants s3:GetObject on the 'my-training-data' bucket, so reading a specific object from that bucket (Option C) is possible. It also grants s3:ListBucket on that bucket, so listing objects (Option D) is possible. The policy includes Athena permissions, so starting a query execution (Option B) is allowed. However, to read Athena query results, the user needs s3:GetObject permission on the S3 location where Athena writes the results, which is typically a different bucket (the query output location). The policy does not grant s3:GetObject on that output bucket, so reading the results (Option A) is not possible.

Answer

Start an Athena query execution

Answer

Read a specific object from the my-training-data bucket

Answer

List objects in the my-training-data bucket

Question 12

The exhibit shows a data quality report for a column named 'age'. Which potential data issue should be investigated further?

Accepted Answer

The minimum age of 0 and maximum age of 120 may be outliers. A minimum age of 0 and maximum age of 120 are potential data entry errors or outliers that warrant further investigation, as extreme values can skew analysis. Option A is incorrect because the mean and median being close suggests the distribution is relatively symmetric and not a data quality issue. Option C is incorrect because a missing value rate of 2.3% is generally considered low and may be handled without major concern. Option D is incorrect because 85 unique values for age is reasonable given the possible age range; it does not indicate a problem.

Answer

The mean and median are significantly different

Answer

The missing value rate of 2.3% is too high

Answer

The number of unique values (85) is too high

Question 13

A data scientist is performing exploratory data analysis on a dataset with missing values. The dataset contains a column 'income' with 20% missing values. The income distribution is right-skewed. Which imputation method is most appropriate to preserve the skewness?

Accepted Answer

Impute with the median income. The median is robust to the right-skewed distribution of income. Imputing with the median preserves the skewness and central tendency without being influenced by outliers, unlike the mean which would pull the imputed values toward the tail and reduce skewness. Option A is wrong because the mean is sensitive to outliers and would distort the distribution. Option C is wrong because dropping rows reduces sample size and may bias the dataset. Option D is wrong because the mode is typically used for categorical data and is not meaningful for continuous skewed data.

Answer

Impute with the mean income

Answer

Drop rows with missing income

Answer

Impute with the mode income

Question 14

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. The Chi-squared test of independence is the appropriate test to assess association between two categorical variables. Here, both the features (categorical) and the target (binary, which is categorical) are categorical, making the Chi-squared test the correct choice. Option A (Pearson correlation) is for continuous variables, not categorical. Option C (ANOVA) compares means across groups for a continuous target, but our target is binary (categorical). Option D (Kolmogorov-Smirnov test) compares distributions of continuous variables, not categorical. Therefore, B is correct.

Answer

Pearson correlation coefficient

Answer

ANOVA

Answer

Kolmogorov-Smirnov test

Question 15

A data scientist is exploring a dataset with a column 'transaction_date'. They want to create features for day of week and month. What is the correct AWS service to schedule a recurring ETL job for this transformation?

Accepted Answer

AWS Glue. AWS Glue is a serverless ETL service that can be scheduled to run recurring jobs for data transformation, such as extracting day of week and month from a date column. Option A is wrong because Amazon Athena is an interactive query service, not an ETL scheduler. Option C is wrong because Amazon SageMaker is a machine learning platform, not an ETL service. Option D is wrong because AWS Lambda can run code on a schedule but is not a full-fledged ETL service for recurring transformations of large datasets.

Answer

Amazon Athena

Answer

Amazon SageMaker

Answer

AWS Lambda