MLS-C01 Exploratory Data Analysis Practice Test 14 — 15 Questions

Question 1

A data scientist is exploring a large dataset (10 TB) stored in Amazon S3. The dataset is in CSV format and has many columns. The scientist wants to quickly compute summary statistics (mean, min, max, count) for each column without moving the data. Which approach is most cost-effective and efficient?

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

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 without moving it. It is cost-effective because you pay only for the data scanned per query. For summary statistics like mean, min, max, count, you can use aggregate functions like AVG, MIN, MAX, COUNT in SQL. Option A (SageMaker Data Wrangler) requires importing data into SageMaker, incurring transfer costs and time. Option B (Amazon EMR) requires provisioning a cluster, which adds overhead and cost for a simple summary task. Option C (S3 Select) works on a single object and cannot compute statistics across entire dataset easily; it is more suited for filtering. Option E (AWS Glue DataBrew) is a data preparation tool that may be more expensive and overkill for simple summary statistics.

Answer

Import the data into Amazon SageMaker Data Wrangler

Answer

Launch an Amazon EMR cluster with Spark

Answer

Use S3 Select to compute statistics

Answer

Use AWS Glue DataBrew to profile the data

Question 2

A data scientist is performing EDA on a dataset with mixed data types (numerical and categorical). Which TWO visualizations are most appropriate for understanding the distribution of categorical features?

Accepted Answer

Pie chart. Bar charts and pie charts are both effective for visualizing the distribution of categorical features. Bar charts display the count or frequency of each category, while pie charts show the relative proportions. Options A (histogram) and B (box plot) are designed for numerical data, and option D (scatter plot) is for the relationship between two numerical variables.

Answer

Histogram

Answer

Box plot

Answer

Scatter plot

Question 3

A data scientist is analyzing a dataset and suspects the presence of outliers that could affect the mean and standard deviation. Which TWO methods are robust to outliers for measuring central tendency and dispersion?

Accepted Answer

Interquartile range (IQR). Median and interquartile range (IQR) are robust to outliers. Mean and standard deviation are sensitive to outliers. Range is also sensitive.

Answer

Range

Answer

Standard deviation

Answer

Mean

Question 4

A data scientist is performing EDA on a dataset with a binary target variable. Which THREE techniques can help assess the relationship between a continuous feature and the target?

Accepted Answer

KDE plot grouped by target. Box plots (comparing distributions for each class), histograms (overlay or side-by-side), and KDE plots (probability density) are all effective for visualizing the relationship between a continuous feature and a binary target. Option A (scatter plot) requires two continuous variables. Option D (bar chart) is for categorical features.

Answer

Scatter plot against another continuous feature

Answer

Bar chart of feature values

Question 5

A data scientist is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?

Accepted Answer

Use AWS Glue DataBrew to create a profile job that outputs data quality reports. AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option A is wrong because SageMaker Data Wrangler requires more manual setup and coding, and is not as straightforward for quick profiling. Option B is wrong because Amazon Athena requires writing SQL queries and is not a dedicated profiling tool. Option D is wrong because AWS Glue ETL with PySpark requires writing custom code.

Answer

Use Amazon SageMaker Data Wrangler to import the data and generate a report

Answer

Use Amazon Athena to run SELECT statements on each column

Answer

Use AWS Glue ETL jobs with PySpark to compute statistics

Question 6

A data scientist is analyzing a dataset with a large number of missing values in several columns. The dataset is stored in an Amazon S3 bucket and is about 5 TB in size. The scientist wants to understand the pattern of missingness (e.g., is it missing completely at random, missing at random, or not missing at random) before deciding on an imputation strategy. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Studio. Which approach should the scientist take to best understand the missing data patterns?

Accepted Answer

Use AWS Glue DataBrew's data quality and missing data reports. AWS Glue DataBrew provides built-in missing data reports that include visualizations such as heatmaps and bar charts to identify patterns of missingness and help determine whether data is MCAR, MAR, or NMAR. Option A is incorrect because SageMaker Data Wrangler, while useful for data preparation, does not have native missingness pattern analysis. Option C is incorrect because AWS Glue ETL jobs require custom PySpark code and are less efficient for exploratory analysis compared to DataBrew's automated reports. Option D is incorrect because while Amazon Athena can query missing values, it lacks pattern analysis capabilities.

Answer

Use Amazon SageMaker Data Wrangler to create a flow and analyze missingness visually

Answer

Use AWS Glue ETL jobs with PySpark to compute missingness statistics

Answer

Use Amazon Athena to run queries to find missing values per column

Question 7

A data scientist is performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?

Accepted Answer

Define reasonable bounds based on domain knowledge and filter or cap the outliers. The most appropriate approach is to define reasonable bounds based on domain knowledge (e.g., 0-120) and filter out or cap the outliers. Option A is incorrect because standard scaling does not handle outliers; it will still be influenced by extreme values. Option B is incorrect because imputing with the mean can distort the distribution when outliers are present. Option D is incorrect because removing the entire column discards valuable information.

Answer

Apply standard scaling to the 'age' column

Answer

Impute the outlier values with the mean of the column

Answer

Remove the 'age' column entirely

Question 8

A data scientist is analyzing a dataset with missing values in several columns. The dataset is stored in an S3 bucket. What is the most efficient method to identify the percentage of missing values per column using AWS services?

Accepted Answer

Use Amazon Athena to query the data with SQL using COUNT(*) and CASE statements to compute missing percentage per column.. Amazon Athena allows running SQL queries directly on data in S3, and the COUNT and CASE statements can compute missing value percentages efficiently without moving data. Option A is wrong because Amazon SageMaker Notebook requires manual coding and is less efficient for quick checks. Option B is wrong because Amazon QuickSight is a visualization tool, not for direct SQL-based analysis. Option D is wrong because AWS Glue Crawler only catalogs metadata, not performing data analysis.

Answer

Use Amazon SageMaker Notebook with pandas to load the dataset and compute missing percentages.

Answer

Use Amazon QuickSight to connect to S3 and calculate missing value percentages via calculated fields.

Answer

Use AWS Glue Crawler to infer schema and view missing values statistics in the AWS Glue Data Catalog.

Question 9

A machine learning engineer is performing exploratory data analysis on a large dataset stored in S3 using Amazon Athena. The dataset contains a timestamp column 'event_time' of type string. The engineer wants to analyze daily trends. Which approach is the most cost-effective and efficient?

Accepted Answer

Partition the table by date derived from the event_time string and query using partition filtering.. Converting the string to a date type in the query allows Athena to use partition pruning if the table is partitioned by date, reducing scanned data. Option A is wrong because creating a view does not reduce data scanned; CAST still processes all rows. Option B is wrong because using CAST in the SELECT statement still scans all data. Option C is wrong because converting to Parquet is beneficial but not the most direct for the given task.

Answer

Create a view that casts the column to timestamp and query the view.

Answer

Use the CAST function in the SELECT statement to convert the string to timestamp.

Answer

Convert the data to Parquet format with a timestamp column and re-query.

Question 10

A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?

Accepted Answer

Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.. Correlation-based feature selection removes highly correlated features, reducing redundancy without distorting the original feature space, and target encoding converts high-cardinality categorical variables into numeric values based on the target mean, which preserves interpretability and avoids dimensionality explosion. Option A is incorrect because PCA reduces interpretability by transforming features into principal components and does not handle categorical variables directly. Option B is incorrect because mutual information is a feature selection method, but label encoding for high-cardinality categoricals can impose arbitrary ordinal relationships. Option C is incorrect because chi-squared test requires categorical features and is not suitable for high-dimensional numerical data; also, one-hot encoding high-cardinality categoricals leads to a drastic increase in dimensionality.

Answer

Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.

Answer

Use mutual information to select top features and apply label encoding to categorical variables.

Answer

Use chi-squared test to select top features and one-hot encode categorical variables.

Question 11

An organization stores streaming data in Amazon Kinesis Data Streams. A data analyst wants to perform real-time exploratory data analysis on the incoming data to detect anomalies. Which AWS service should the analyst use to run SQL queries on the streaming data?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time, which is exactly what the data analyst needs for real-time exploratory data analysis and anomaly detection. Option B (Amazon SageMaker) is incorrect because it is a machine learning service for building and training models, not for running SQL on streaming data. Option C (AWS Glue) is incorrect because it is a serverless ETL service for batch processing, not real-time SQL. Option D (Amazon Athena) is incorrect because it is an interactive query service for analyzing data in S3 using SQL, but it is designed for batch queries on static data, not streaming data.

Answer

Amazon SageMaker

Answer

AWS Glue

Answer

Amazon Athena

Question 12

A data scientist is using Amazon SageMaker Data Wrangler for exploratory data analysis. The dataset contains a column with missing values that are encoded as 'NA' strings. The data scientist wants to treat these as missing values during the import. Which step should the data scientist take?

Accepted Answer

Configure a custom missing value symbol 'NA' in the import settings of Data Wrangler.. Amazon SageMaker Data Wrangler allows specifying custom missing value symbols during the import step. By configuring 'NA' as a custom missing value symbol in the import settings, Data Wrangler will automatically treat 'NA' strings as missing values when reading the dataset. Option B is incorrect because the 'Impute' transform is used to fill missing values after they have been recognized as missing; it does not handle the initial identification of 'NA' strings as missing. Option C is incorrect because using 'Replace missing' after import is less efficient and not the recommended approach; it is better to handle it during import to ensure downstream transforms treat the values correctly. Option D is incorrect because dropping rows with 'NA' prematurely discards data before any analysis; the goal is to treat 'NA' as missing, not to remove the rows.

Answer

Use the 'Impute' transform to fill 'NA' with the mean of the column.

Answer

Use the 'Replace missing' transform to replace 'NA' with null after import.

Answer

Use the 'Drop missing' transform to remove rows containing 'NA'.

Question 13

A data engineer is performing EDA on a dataset with 1 million rows and 200 columns. The dataset is stored in S3 as CSV files. The engineer notices that some columns have a high proportion of zeros. What is the best approach to determine if these zeros represent missing data or actual zero values?

Accepted Answer

Consult the data source documentation or domain experts to understand the meaning of zero values.. Domain knowledge and documentation are the most reliable ways to understand the meaning of zeros. Option A is wrong because statistical methods cannot distinguish missing vs actual zero without context. Option B is wrong because metadata may not have this detail. Option C is wrong because comparing to other columns might be misleading.

Answer

Check correlation of zero columns with other features; if low, assume zeros are missing.

Answer

Calculate the percentage of zeros and compare with other columns; if unusually high, treat as missing.

Answer

Use AWS Glue Data Catalog to view column statistics and infer missing values.

Question 14

A data scientist is analyzing a dataset with a skewed target variable for a regression problem. During EDA, the scientist wants to transform the target variable to approximate a normal distribution. Which transformation should the scientist apply first?

Accepted Answer

Box-Cox transformation. Box-Cox transformation (D) is a parametric transformation that identifies the optimal power transformation to make data more normally distributed. For skewed target variables in regression, it is often preferred as a first approach because it can handle various skewness patterns and includes log transformation as a special case (lambda=0). Quantile transformation (A) is non-parametric and can overfit; Min-Max scaling (B) only rescales range, not shape; Log transformation (C) is a specific case that works for positive data but may not be optimal for all skewness. Therefore, Box-Cox is the best first choice.

Answer

Quantile transformation

Answer

Min-Max scaling

Answer

Log transformation

Question 15

A data analyst is using Amazon QuickSight to explore a dataset with 10 million rows. The analyst wants to create a histogram of a numerical column. However, the query is taking too long. Which action should the analyst take to improve performance without losing accuracy?

Accepted Answer

Import the dataset into SPICE (Super-fast, Parallel, In-memory Calculation Engine).. SPICE (Super-fast, Parallel, In-memory Calculation Engine) is Amazon QuickSight's in-memory engine that caches data, enabling fast query performance without losing accuracy. Importing the dataset into SPICE speeds up histogram computation while preserving full data. Option A is incorrect because using Athena with a limit clause reduces the number of rows, losing accuracy. Option B is incorrect because reducing bins changes histogram granularity, not necessarily improving performance and potentially losing detail. Option C is incorrect because sampling reduces accuracy by excluding data points.

Answer

Change the data source to Amazon Athena directly with a limit clause.

Answer

Reduce the number of bins in the histogram.

Answer

Use a sample of the data (e.g., 1 million rows) for the histogram.