MLS-C01 Exploratory Data Analysis Practice Test 31 — 15 Questions

Question 1

A data scientist is performing EDA on a large dataset stored in Amazon S3 as Parquet files, partitioned by date. The dataset contains 500 million rows and 100 columns, and the scientist needs to quickly compute summary statistics and correlations between numerical features without loading the entire dataset into memory. The scientist wants to minimize both cost and time. Which AWS service should the scientist use to run SQL queries directly on the S3 data and generate the required statistics?

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

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it enables serverless SQL queries directly against data in Amazon S3, eliminating the need to load or copy data. It supports Parquet, which is columnar and efficient for analytical queries, and can compute summary statistics and correlations using SQL. This minimizes cost and time, aligning with the requirement to quickly explore a large dataset without managing infrastructure.

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AWS Glue

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Amazon Redshift Spectrum

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Amazon EMR

Question 2

A data scientist is exploring a dataset in Amazon SageMaker Studio and wants to detect anomalies in a univariate time series of sensor readings. The data is approximately normally distributed, and the scientist needs a simple statistical method to flag outliers that are more than three standard deviations from the mean. Which method should the scientist use?

Accepted Answer

Z-score. The Z-score is the correct method because it quantifies the distance of a data point from the mean in units of standard deviation. For normally distributed data, a threshold of three standard deviations is a common rule for outlier detection. This method is straightforward to implement and interpret, and it directly satisfies the requirement to flag points more than three standard deviations from the mean.

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DBSCAN clustering

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Isolation Forest

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Interquartile range (IQR) method

Question 3

A data scientist is preparing a dataset for a binary classification model. The dataset has a numerical feature 'income' that is heavily right-skewed. During exploratory data analysis, the scientist wants to reduce the skewness to make the feature more suitable for a linear model. Which transformation should the scientist apply to the 'income' feature?

Accepted Answer

Apply a logarithmic transformation (e.g., log1p) to the feature.. The income feature is right-skewed, meaning it has a long right tail. A logarithmic transformation compresses the high end of the distribution and expands the low end, making it more symmetric. This is a common technique in exploratory data analysis to satisfy the normality assumption of linear models. Standardization, one-hot encoding, and discretization do not alter the skewness in the desired way.

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Apply one-hot encoding to the feature.

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Standardize the feature by subtracting the mean and dividing by the standard deviation.

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Discretize the feature into equal-width bins.

Question 4

A financial analytics team stores daily transaction records in an Amazon S3 bucket as Parquet files. They need to perform exploratory data analysis on a 2 TB dataset to compute summary statistics and visualize correlations between features. The team wants to minimize data movement and cost while using a familiar SQL-like interface. Which AWS service should they use?

Accepted Answer

Amazon Athena. Amazon Athena provides a serverless, SQL-based way to analyze data directly in Amazon S3, which is well-suited for exploratory data analysis on large Parquet datasets. It avoids data movement and infrastructure management, making it cost-effective for computing summary statistics and correlations.

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AWS Glue DataBrew

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Amazon SageMaker Data Wrangler

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Amazon Redshift Spectrum

Question 5

A machine learning engineer is exploring a dataset stored in Amazon S3 that contains 50 million records of customer transactions in Parquet format. The engineer needs to quickly calculate summary statistics (mean, median, standard deviation) for several numerical columns and also visualize the distribution of transaction amounts. The engineer wants to minimize data movement and avoid loading the entire dataset into memory. Which approach is MOST efficient for this exploratory data analysis?

Accepted Answer

Use Amazon Athena to run SQL queries that compute the statistics directly on the Parquet data in S3, and then use Amazon QuickSight to visualize the distribution.. Amazon Athena queries data directly in Amazon S3 without loading it into a separate system, which minimizes data movement and leverages the columnar Parquet format for efficient scans. It supports SQL aggregate functions for summary statistics and integrates with Amazon QuickSight for visualization. This serverless approach avoids cluster management and memory constraints, making it ideal for quick EDA on large datasets.

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Download the Parquet files to a local machine, load them into a pandas DataFrame, and use pandas profiling to compute the statistics and plots.

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Launch an Amazon EMR cluster with Apache Spark, read the Parquet data into a Spark DataFrame, and use Spark SQL to compute the statistics and generate plots.

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Use AWS Glue to crawl the S3 data and create a Data Catalog table, then use Amazon Redshift Spectrum to query the data and compute the statistics.

Question 6

A data scientist is performing exploratory data analysis on a dataset with 50 features. The scientist wants to identify pairs of numerical features that are highly linearly related to avoid multicollinearity in a subsequent linear model. The scientist also wants to visualize the relationships between multiple features simultaneously. Which two techniques should the scientist use? (Choose two.)

Accepted Answer

Create a scatterplot matrix (pairplot). Computing the Pearson correlation coefficient matrix and creating a scatterplot matrix are the two techniques that directly address the need to identify highly linearly related feature pairs and visualize multiple feature relationships. The correlation matrix quantifies linear relationships, while the scatterplot matrix provides a visual overview, together enabling effective detection of multicollinearity and exploration of feature interactions.

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Calculate variance inflation factor (VIF) for each feature

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Perform principal component analysis (PCA)

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Use t-SNE for dimensionality reduction

Question 7

A data scientist must explore a 25 GB dataset stored in Amazon S3 to compute summary statistics and correlation coefficients before modeling. The data is already cataloged in the AWS Glue Data Catalog. The scientist wants to write standard SQL and avoid loading the full dataset into memory. Which approach is MOST appropriate?

Accepted Answer

Run SQL queries with Amazon Athena against the cataloged table and inspect the results. Athena is serverless and reads S3 data in place through the Glue Data Catalog, letting the scientist write SQL aggregates including corr for correlation without moving or fully materializing the data. Local pandas loading, Redshift COPY, and Kinesis Data Analytics all either exceed the memory constraint, duplicate the data, or target the wrong data paradigm.

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Download all objects to a local workstation and load them into pandas DataFrames

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Use Amazon Kinesis Data Analytics to run SQL over the S3 objects

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Create an Amazon Redshift cluster and COPY the S3 data into it before querying

Question 8

A data scientist is exploring a dataset stored in Amazon S3 and wants to quickly generate visualizations such as scatter plots and histograms without writing code. The dataset is in CSV format and is moderately sized (under 1 GB). Which AWS service provides a visual interface for exploratory data analysis and feature engineering?

Accepted Answer

Amazon SageMaker Data Wrangler. SageMaker Data Wrangler is purpose-built for visual exploratory data analysis and feature engineering. It allows data scientists to quickly load data from S3, generate visualizations, and apply transformations without writing code, which matches the requirement.

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Amazon QuickSight

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AWS Glue DataBrew

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Amazon Athena

Question 9

A data scientist is analyzing a dataset with a categorical feature 'product_category' that has 50 unique values. The target variable is binary. The scientist wants to understand the relationship between 'product_category' and the target. Which technique is most appropriate for this exploratory analysis?

Accepted Answer

Calculate the chi-square statistic and p-value for the contingency table.. The chi-square test is designed to determine whether two categorical variables are independent. With a categorical feature and a binary target, it is the appropriate statistical test. It compares observed frequencies in each category to expected frequencies if there were no association. Other methods like correlation or PCA are not suitable for nominal categorical data and can produce misleading results.

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Perform a principal component analysis (PCA) on the one-hot encoded category and the target.

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Compute the Pearson correlation coefficient between the encoded category and the target.

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Create a scatter plot of the category codes against the target values.

Question 10

A data scientist is profiling a 40-column dataset in Amazon SageMaker Data Wrangler and notices that the 'customer_tenure_months' column shows a near-zero variance but also a high count of missing values, while 'monthly_charges' shows a normal distribution with a few extreme values. The scientist wants to quantify how strongly each numeric feature is linearly related to the continuous target 'lifetime_value' before deciding which features to keep. Which Data Wrangler analysis should the scientist add to the flow?

Accepted Answer

A Correlation (numeric) analysis with lifetime_value as the target column.. The goal is to quantify linear association between each numeric feature and a continuous target. Data Wrangler's Correlation analysis accepts a target column and returns Pearson correlation coefficients per numeric feature, which is exactly the metric needed to rank and filter predictors. Descriptive summaries and model-based impact scores answer different questions, and leakage detection guards data quality rather than relationship strength.

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A Target Leakage analysis with lifetime_value as the target column and a 0.8 threshold.

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A Quick Model analysis targeting lifetime_value, then sort features by their impact score.

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A Multicolumn Summary analysis, then inspect the correlation values against lifetime_value.

Question 11

A data scientist is exploring a dataset with a highly skewed numerical feature that has a long right tail. The scientist wants to visualize the distribution to decide on a transformation. Which visualization technique is BEST suited for this purpose?

Accepted Answer

A histogram with a logarithmic scale on the x-axis.. A histogram with a logarithmic x-axis is ideal for visualizing a highly skewed feature because it compresses the long tail and reveals the underlying distribution shape. This helps determine if a log transformation is appropriate and provides a clearer view of the data's spread and central tendency.

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A box plot of the feature grouped by a categorical variable.

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A line plot of the feature sorted by value.

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A scatter plot of the feature against another numerical feature.

Question 12

A data scientist is running exploratory data analysis in a SageMaker Studio notebook on a 12 GB CSV file stored in Amazon S3. The notebook kernel keeps running out of memory when a pandas DataFrame is loaded with pd.read_csv(). The scientist needs to compute column-level summary statistics and correlation values without downloading the entire file into memory. Which approach should the scientist use?

Accepted Answer

Load the file with Dask or PySpark using chunked or distributed reads, then compute describe() and corr() on the resulting collection.. Chunked or distributed readers such as Dask and PySpark keep the working set bounded by partition size rather than total file size, which is exactly what the notebook needs. They also ship describe() and corr() equivalents, so the scientist gets the same column statistics and correlation values that pandas would produce, but computed partition-wise. Larger instances or columnar formats still require materializing the full table in the kernel.

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Increase the Studio notebook instance to a larger memory-optimized instance and rerun pd.read_csv() on the full file.

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Convert the CSV to Apache Parquet with AWS Glue, then call pandas.read_parquet() to load the full dataset before computing statistics.

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Use AWS Glue DataBrew to create a profile job against the S3 prefix and read the generated statistics from the job output.

Question 13

A data scientist suspects that the 'signup_date' timestamp column in a customer dataset contains parsing problems, because several rows fail downstream feature extraction. The scientist wants to inspect the detected data types, missing-value counts, and value distributions for that column without writing transformation code. Which capability should be used?

Accepted Answer

Configure a SageMaker Data Wrangler data quality and insights report on the dataset. The requirement is code-free profiling of a suspicious column: inferred type, missing values, and distribution. The Data Wrangler data quality and insights report provides precisely that profiling output as a managed job. Ground Truth is for labeling, Model Monitor is for deployed-endpoint drift detection, and Debugger observes training tensors, so none of them profile an offline column for parsing problems.

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Run a SageMaker Debugger rule on the dataset to detect the timestamp anomalies

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Create a SageMaker Ground Truth labeling job with a date-annotation task type

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Enable SageMaker Model Monitor on the dataset and read the data quality baseline violations

Question 14

A data analyst wants to interactively explore a 40 GB dataset in Amazon S3 with SQL and immediately see result tables and simple charts, without provisioning a database or writing ETL code. The analyst has no preference for a specific query engine and wants the least operational effort. Which AWS service should the analyst use?

Accepted Answer

Amazon Athena with a workgroup configured to output query results to S3.. Athena queries data in place in S3 using the Glue Data Catalog for schema, so no cluster or warehouse provisioning is needed. The analyst can run SQL, view paginated result sets in the console, and download or visualize results with a connected BI tool. EMR, Redshift Spectrum, and RDS all require standing up and managing compute or storage resources that exceed the stated requirement.

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Amazon RDS for PostgreSQL with the aws_s3 extension to import the S3 objects into tables.

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Amazon EMR with a transient cluster running Apache Hive on the dataset in S3.

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Amazon Redshift Spectrum with an external schema pointing at the S3 prefix.

Question 15

A data scientist is running an Amazon SageMaker Studio notebook to explore a 40 GB Parquet dataset stored in Amazon S3. Queries against the full dataset take several minutes and repeatedly exhaust the notebook instance memory. The scientist wants to interactively explore subsets and compute summary statistics without provisioning a larger instance or copying the data. Which approach best meets these requirements?

Accepted Answer

Use Amazon Athena to query the Parquet data in place and read the result sets from the notebook with the Athena API or JDBC driver.. Athena is the right fit because it separates compute from storage and queries Parquet in S3 in place, so the notebook never has to hold the dataset in memory. Columnar projection and predicate pushdown keep scans small, and results stream back in pages. The other approaches either require moving or duplicating the data, or they still execute on a memory-bound notebook or ML instance.

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Convert the Parquet files to CSV in S3 and load them into the notebook with pandas read_csv in chunks.

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Enable Amazon SageMaker Data Wrangler and import the full dataset into a Data Wrangler flow for interactive analysis.

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Register the S3 prefix as an Amazon SageMaker Feature Store feature group and query it with the SageMaker Studio feature store UI.