MLS-C01 Exploratory Data Analysis Practice Test 19 — 15 Questions

Question 1

A data analyst is performing exploratory data analysis on a dataset and notices that there are outliers in several numerical columns. Which TWO methods can the analyst use to identify outliers?

Accepted Answer

Calculate z-scores and flag any data points with |z| > 3.. Options B and C are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles (option C). Z-scores identify outliers as points with |z| > 3, assuming a roughly normal distribution (option B). Option A (scatter plot matrix) can help visualize outliers but is not a systematic detection method. Option D (comparing mean and median) provides insight into skewness but does not directly flag outliers. Option E (histogram) shows distribution shape but requires subjective judgment to identify outliers.

Answer

Create a scatter plot matrix to visually inspect.

Answer

Compare the mean and median of each column.

Answer

Plot a histogram and look for gaps.

Question 2

A data scientist is exploring a dataset stored as a single 2 GB object in S3. The scientist wants to read only a subset of the file (e.g., the first 1000 lines) to perform initial data inspection. Which approach should the scientist take to minimize data transfer and cost?

Accepted Answer

Use S3 Select with a SQL query to retrieve the first 1000 rows.. S3 Select enables retrieving a subset of data using SQL queries, such as SELECT * FROM s3object LIMIT 1000, minimizing data transfer and cost. Option A is inefficient as it downloads the entire 2 GB file. Option C retrieves bytes, not lines, and may still transfer excessive data or require multiple requests to locate line boundaries. Option D scans the entire file with Athena, incurring cost and latency for a full table scan even with LIMIT 1000.

Answer

Use the AWS CLI to download the entire file and then use head to get the first lines.

Answer

Use the S3 Range header to read the first 1 MB of the file and parse lines.

Answer

Use Amazon Athena to query the file with LIMIT 1000.

Question 3

A data scientist is trying to list objects in an S3 bucket named 'my-bucket' using the AWS CLI command: `aws s3 ls s3://my-bucket/`. The command fails with an access denied error. The IAM policy attached to the scientist's role is shown in the exhibit. What is the most likely cause of the failure?

Accepted Answer

The condition on the ListBucket action requires all objects to have the tag 'data-type'='training', which may not be satisfied.. The IAM policy includes the s3:ListBucket action with a condition that uses s3:ExistingObjectTag to require each object to have the tag 'data-type' set to 'training'. However, the ListBucket operation lists all objects in the bucket, and the condition is evaluated against each object. If any object in the bucket does not have this tag, the request fails with an access denied error. Option B is incorrect because the policy does include s3:ListBucket. Option C is incorrect because the bucket name 'my-bucket' is a valid resource identifier; the full ARN is not required. Option D is incorrect because the StringEquals operator is valid for this condition key; the issue is the condition's requirement, not the operator.

Answer

The IAM policy does not include the s3:ListBucket action.

Answer

The policy does not grant access to the bucket because it uses 'my-bucket' instead of the full ARN.

Answer

The condition should use 'StringLike' instead of 'StringEquals'.

Question 4

A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?

Accepted Answer

The first query uses a partition column (year), allowing partition pruning, while the second query does not.. The table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions, resulting in a longer execution time. Option A is incorrect because compression does not directly affect partition pruning; it reduces storage size but not scan time in this context. Option C is incorrect because the data format (Parquet) could help with columnar pruning, but the key difference here is partition pruning, not file format. Option D is incorrect because using SELECT * does not inherently cause slow performance; the lack of partition pruning is the main issue.

Answer

The data is compressed, and the first query benefits from compression.

Answer

The data is stored in Parquet format, which is optimized for columnar access.

Answer

The second query is not optimized because it uses 'SELECT *'.

Question 5

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler provides built-in data analysis and visualization capabilities, including correlation analysis, making it suitable for this task. Amazon QuickSight is a BI tool for dashboards, not for feature correlation analysis. Amazon Athena is a query service for data in S3, not for embedded data wrangling. AWS Glue DataBrew is a visual data preparation tool, but SageMaker Data Wrangler is more directly suited for correlation analysis.

Answer

Amazon QuickSight

Answer

Amazon Athena

Answer

AWS Glue DataBrew

Question 6

A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?

Accepted Answer

Use Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.. The most appropriate approach for understanding the distribution of numeric features for each class is to use histograms grouped by the class label. Amazon SageMaker Data Wrangler (option C) can generate these histograms, providing a clear visual comparison of how each numeric feature is distributed across class A and class B. This is especially useful with imbalanced data (95% vs 5%) because it reveals differences in shape, central tendency, and spread without being influenced by class frequencies. Option A (t-test) tests for statistical significance but does not visualize the distribution. Option B (box plots) can show summary statistics but not the full distribution shape as effectively as histograms. Option D (correlation matrix) measures linear relationships with the target but does not show per-class feature distributions.

Answer

Run a t-test for each feature to determine statistical significance between classes.

Answer

Generate box plots for each feature using Amazon QuickSight.

Answer

Compute the correlation matrix between features and the target.

Question 7

A data engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 (10 TB in CSV format). The dataset has 2000 columns and 50 million rows. The engineer needs to compute summary statistics (mean, median, standard deviation) for each numeric column and identify missing values. Which approach is MOST cost-effective and time-efficient?

Accepted Answer

Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.. Using Amazon Athena with columnar formats like Parquet after converting from CSV reduces query costs and improves performance. Option A (Redshift Spectrum) requires setting up a Redshift cluster, which is overkill. Option B (SageMaker Data Wrangler) may struggle with 2000 columns. Option D (AWS Glue ETL) is more expensive and slower for simple statistics.

Answer

Use Amazon Redshift Spectrum to query the data directly from S3.

Answer

Load the data into Amazon SageMaker Data Wrangler and compute statistics interactively.

Answer

Use AWS Glue ETL to compute statistics and write results to S3.

Question 8

A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying to the target variable to improve model performance?

Accepted Answer

Apply log transformation to the target variable.. Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.

Answer

Apply StandardScaler to the target variable.

Answer

Apply MinMaxScaler to the target variable.

Answer

Apply one-hot encoding to the target variable.

Question 9

A data scientist is working with a dataset containing customer transactions. The dataset has a column named 'transaction_date' with timestamp values. The scientist wants to create new features such as day of week, hour, and whether the transaction occurred on a weekend. Which AWS service provides built-in feature engineering capabilities for datetime columns?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler includes built-in transformations for datetime features like extracting day, month, hour, etc. Option B (Amazon Athena) can extract parts but not as a feature engineering step. Option C (AWS Glue ETL) requires custom code. Option D (Amazon EMR) requires more manual effort.

Answer

Amazon Athena

Answer

AWS Glue ETL

Answer

Amazon EMR

Question 10

A data scientist is analyzing clickstream data from a website. The data is stored in Amazon S3 as JSON files, each containing nested arrays. The scientist needs to flatten the nested structures and compute user session durations. Which approach is most efficient for this EDA task?

Accepted Answer

Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration.. AWS Glue DataBrew provides a visual interface to flatten nested JSON and compute derived metrics like session duration without writing code. Option A (EMR with Apache Spark) is more complex and requires writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option D (QuickSight) is visualization only and cannot flatten or compute session duration.

Answer

Use Amazon EMR with Apache Spark to process the data.

Answer

Use Amazon Athena with JSON SerDe to query the data and compute session duration with SQL.

Answer

Use Amazon QuickSight to visualize the raw data without flattening.

Question 11

During exploratory data analysis, a data scientist notices that a categorical feature 'city' has over 1,000 unique values. The dataset has 10,000 rows. Which technique should the scientist consider to reduce the cardinality of this feature?

Accepted Answer

Group low-frequency categories into a single 'other' category.. Grouping rare categories into an 'other' bucket is a common technique to reduce cardinality. Option A (label encoding) assigns numeric labels but still has 1000 unique values. Option B (grouping into 'other') reduces cardinality. Option C (one-hot encoding) would create too many columns. Option D (frequency encoding) replaces categories with frequency but still has 1000 values.

Answer

Apply label encoding to assign numeric labels.

Answer

Apply one-hot encoding to all categories.

Answer

Apply frequency encoding to replace each category with its frequency.

Question 12

A data scientist is performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?

Accepted Answer

Calculate mutual information between each feature and the target.. Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (ANOVA) compares means across groups but assumes linearity. Option B (Pearson correlation) only captures linear relationships. Option D (Chi-squared test) is for categorical features, not suitable for the mix of numeric and categorical features.

Answer

Use ANOVA to compare feature means across target classes.

Answer

Compute Pearson correlation coefficients.

Answer

Perform chi-squared tests for each feature.

Question 13

A data engineer is running an Amazon SageMaker Data Wrangler flow on a dataset with 5 million rows. The flow includes several transformations. The engineer wants to validate the data quality by checking for missing values and outliers before training. Which approach is most efficient?

Accepted Answer

Use Data Wrangler's data quality and insights report to generate a report with statistics and visualizations.. Using Data Wrangler's built-in data quality and insights report is the most efficient way to get statistics and detect issues without custom code. Option B (Athena) requires writing SQL queries. Option C (QuickSight) needs exporting. Option D (EMR) is overkill.

Answer

Export the transformed data to S3 and query with Amazon Athena.

Answer

Use Amazon EMR with Spark to compute statistics.

Answer

Import the data into Amazon QuickSight and create dashboards.

Question 14

Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Remove rows or columns with missing values if they are few.. The correct techniques for handling missing values are removing rows/columns with missing values (if the proportion is small) and imputing missing values with statistical measures like the mean or median. Options A (feature scaling), C (PCA), and D (one-hot encoding) are not methods for dealing with missing data; they serve other purposes such as normalization, dimensionality reduction, and encoding categorical variables.

Answer

Apply feature scaling to normalize the data.

Answer

Use Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Apply one-hot encoding to the missing values.

Question 15

Which THREE of the following are valid techniques for detecting outliers in a dataset during exploratory data analysis? (Select THREE.)

Accepted Answer

Z-score method: flag points with absolute Z-score > 3.. Z-score, IQR, and Isolation Forest are all common outlier detection methods. Option B (Linear regression) is not for outlier detection; it models relationships between variables. Option D (K-means) is a clustering algorithm, not primarily for outlier detection.

Answer

Linear regression residuals.

Answer

K-means clustering.