MLS-C01 Exploratory Data Analysis Practice Test 17 — 15 Questions

Question 1

Refer to the exhibit. A data scientist is unable to query a table in Amazon Athena that is located in the 'my-data-bucket' S3 bucket. The IAM policy shown is attached to the scientist's role. What is the most likely reason for the failure?

Accepted Answer

The policy does not allow s3:PutObject to write query results to an S3 bucket.. Athena queries also require permission to write query results to a S3 bucket, typically specified as 's3:PutObject' on an output location. The policy lacks that permission. Option A is wrong because the policy allows s3:GetObject and s3:ListBucket. Option C is wrong because athena:StartQueryExecution is allowed. Option D is wrong because there is no encryption restriction in the policy.

Answer

The policy does not allow decrypting data encrypted with AWS KMS.

Answer

The policy does not allow athena:StartQueryExecution.

Answer

The policy does not allow s3:GetObject on the bucket.

Question 2

Refer to the exhibit. A data scientist runs the AWS CLI command shown and gets the output. The scientist wants to create an Athena table over all log files in the 'logs/2023/' prefix, including files smaller than 1000 bytes. Which approach achieves this?

Accepted Answer

Create the table using LOCATION 's3://my-bucket/logs/2023/' which includes all files under that prefix.. The CLI command filters objects larger than 1000 bytes, but the scientist wants all files in the prefix. The Athena table definition should point to the entire prefix without size filtering. Option A is wrong because the command was run locally, not affecting Athena. Option B is wrong because adding a WHERE clause in Athena only filters after scanning. Option D is wrong because the scientist can still create the table without size restrictions.

Answer

Create the table and add a WHERE clause to include small files.

Answer

Ask the S3 team to remove the size restriction on the bucket.

Answer

Modify the CLI command to remove the size filter and re-run it before creating the table.

Question 3

Refer to the exhibit. A data scientist is using AWS Glue ETL jobs to process data from a source database. The job logs show repeated timeout errors. Which EDA step should the scientist perform to diagnose the issue?

Accepted Answer

Check the source database table sizes and row counts over time.. The timeout suggests the job is taking longer than the max 300 seconds. Analyzing source data volume trends helps determine if data size has increased, causing longer processing time. Option A is wrong because changing job type does not address root cause. Option B is wrong because increasing timeout without understanding data growth is a temporary fix. Option D is wrong because the error is about timeout, not connectivity.

Answer

Test network connectivity from the Glue job to the source database using telnet.

Answer

Switch the Glue ETL job type from Spark to Python shell to reduce overhead.

Answer

Increase the Glue job timeout to 600 seconds and rerun.

Question 4

A data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?

Accepted Answer

Create scatter plots of numerical features against the target variable.. Scatter plots for numerical vs continuous target and box plots for categorical vs continuous target are standard. Option C (confusion matrix) is for classification. Option D (histogram of target) is univariate. Option E (silhouette score) is for clustering.

Answer

Generate a confusion matrix for the target variable.

Answer

Compute the silhouette score for each feature.

Answer

Plot a histogram of the target variable.

Question 5

A data scientist is analyzing a dataset with many missing values. The scientist wants to decide on an imputation strategy. Which THREE considerations are important for choosing the imputation method?

Accepted Answer

The mechanism of missingness (MCAR, MAR, MNAR).. Missing data mechanism (MCAR/MAR/MNAR), proportion of missing values, and feature distribution (skewness, outliers) all affect imputation choice. Option A (feature importance) is not directly relevant. Option D (class imbalance) is for classification targets.

Answer

The class imbalance of the target variable.

Answer

The feature importance according to a random forest model.

Question 6

A data scientist is exploring a dataset and wants to identify outliers in a numerical feature. The feature is not normally distributed. Which technique is robust to non-normal distributions?

Accepted Answer

Use the IQR method: flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.. The Interquartile Range (IQR) method does not assume normality and uses percentiles. Option A (Z-score) assumes normality. Option C (mean ± 3σ) also assumes normality. Option D (MAD) is robust but Z-score based; IQR is more common for non-normal.

Answer

Compute the Median Absolute Deviation (MAD) and flag values with MAD > 3.

Answer

Calculate the Z-score and flag values with |Z| > 3.

Answer

Flag values more than 3 standard deviations from the mean.

Question 7

A data scientist is performing EDA on a time series dataset of daily website visits. The scientist wants to identify any seasonality patterns. Which visualization is most appropriate?

Accepted Answer

Line plot with day on x-axis and visits on y-axis, highlighting weekends.. A time series line plot with marked intervals (e.g., weekly, monthly) can reveal seasonal patterns. Option A (histogram) shows distribution, not time patterns. Option B (scatter plot of visits vs day) is essentially a line plot but less effective for seasonality. Option C (correlation matrix) does not show temporal patterns.

Answer

Correlation matrix of visits with lagged versions of itself.

Answer

Scatter plot of visits against the day of the month.

Answer

Histogram of daily visit counts.

Question 8

Refer to the exhibit. A data scientist queries the table with 'SELECT COUNT(*) FROM mytable' in Athena and gets a result of 1000 rows. However, the scientist knows there are 1500 data files in the S3 location. What is the most likely reason for the discrepancy?

Accepted Answer

Some files may use a different delimiter (e.g., tab) and are not parsed correctly, resulting in zero rows from those files.. The table is not partitioned (empty PartitionKeys), and the SerDe expects comma-delimited files. If some files use a different delimiter (e.g., tab), those rows may not be parsed correctly, leading to fewer rows counted. Option A is wrong because the table schema matches the data. Option B is wrong because Athena can handle many small files, but count should still include all rows. Option D is wrong because Athena does not skip files based on size unless explicitly filtered.

Answer

The table schema does not match the data, causing some files to be skipped.

Answer

Some files may be empty or contain only headers, so they contribute 0 rows.

Answer

Athena skips files larger than a certain size to prevent scanning too much data.

Question 9

A data scientist is exploring a dataset with many features and wants to detect multicollinearity. Which technique should the scientist use?

Accepted Answer

Calculate the Variance Inflation Factor (VIF) for each feature.. Variance Inflation Factor (VIF) is a standard metric for detecting multicollinearity. Option A (pairwise scatter plots) can hint but not quantify. Option B (Pearson correlation matrix) shows pairwise linear correlation but not multicollinearity among multiple variables. Option D (ANOVA) is for comparing means.

Answer

Compute the Pearson correlation matrix between features.

Answer

Perform ANOVA on each feature against the target.

Answer

Create pairwise scatter plots of all features.

Question 10

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains customer demographic information and purchase history. Which approach should the data scientist take to handle missing values without introducing bias into the dataset?

Accepted Answer

Use multiple imputation to estimate missing values.. Option C is correct because multiple imputation accounts for the uncertainty of missing values by creating multiple imputed datasets and combining results, reducing bias compared to single imputation or deletion methods. Option A is wrong because dropping rows with missing values can introduce bias if the missingness is not completely random. Option B is wrong because mean imputation can reduce variance and bias relationships. Option D is wrong because using a constant value (e.g., 0) is arbitrary and can distort the data distribution.

Answer

Drop all rows with any missing values.

Answer

Impute missing values with the mean of each column.

Answer

Replace missing values with a constant, such as 0.

Question 11

A machine learning engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 using AWS Glue. The dataset contains a mix of numeric and categorical features. The engineer wants to efficiently compute summary statistics (e.g., mean, median, standard deviation) for the numeric columns. Which AWS service or feature should the engineer use to achieve this with minimal setup?

Accepted Answer

Use AWS Glue DataBrew to profile the dataset.. Option B is correct because AWS Glue DataBrew provides a visual interface to profile data and compute summary statistics without writing code. Option A is wrong because Amazon Athena requires SQL queries and more manual effort. Option C is wrong because Amazon EMR requires cluster setup and management. Option D is wrong because Amazon SageMaker Data Wrangler is a good tool but requires more configuration than DataBrew for simple summary statistics.

Answer

Launch an Amazon EMR cluster and use Spark.

Answer

Use Amazon Athena to run SQL queries on the data.

Answer

Use Amazon SageMaker Data Wrangler.

Question 12

A data analyst is exploring a dataset with a target variable that is highly imbalanced. The minority class represents only 1% of the data. Which technique should the analyst use to better understand the relationships between features and the minority class?

Accepted Answer

Use stratified sampling to create a balanced sample for analysis.. Option A is correct because stratified sampling ensures the minority class is proportionally represented in the sample, allowing meaningful analysis. Option B is wrong because random sampling may miss the minority class entirely. Option C is wrong because SMOTE is for generating synthetic data, not for exploratory analysis. Option D is wrong because feature scaling does not address class imbalance.

Answer

Apply SMOTE to the dataset before analysis.

Answer

Use random sampling to reduce the dataset size.

Answer

Scale the features using Min-Max scaling.

Question 13

A data scientist is analyzing a time series dataset of daily website traffic. The scientist notices a strong weekly seasonality. To better understand the underlying patterns, which decomposition method should the scientist use to separate the trend, seasonal, and residual components?

Accepted Answer

Use STL (Seasonal and Trend decomposition using Loess).. Option C is correct because STL decomposition is robust to outliers and can handle any seasonality period, making it suitable for daily data with weekly seasonality. Option A is wrong because classical decomposition assumes additive seasonality and is less robust. Option B is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing model, not primarily for decomposition.

Answer

Additive decomposition using moving averages.

Answer

Fit an ARIMA model and examine residuals.

Answer

Apply an ETS (Error, Trend, Seasonal) model.

Question 14

A machine learning team is working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?

Accepted Answer

Truncated Singular Value Decomposition (SVD).. Option D is correct because Truncated SVD (e.g., using sklearn's TruncatedSVD or PCA on sparse data via SVD) is designed for sparse matrices and preserves variance. Option A is wrong because PCA with covariance matrix requires dense matrix and is computationally expensive for sparse data. Option B is wrong because t-SNE is for visualization, not for general dimensionality reduction preserving structure. Option C is wrong because LDA is a supervised method and requires labels.

Answer

t-distributed Stochastic Neighbor Embedding (t-SNE).

Answer

Linear Discriminant Analysis (LDA).

Answer

Principal Component Analysis (PCA) using the covariance matrix.

Question 15

A data analyst is examining the distribution of a continuous variable and notices that its histogram is heavily skewed to the right. Which transformation should the analyst apply to make the distribution more symmetrical?

Accepted Answer

Logarithmic transformation (log).. Option B is correct because log transformation is commonly used to reduce right skewness by compressing the long tail. Option A is wrong because the square root transformation is less effective for severe skewness. Option C is wrong because Box-Cox requires all values positive and is a family that includes log, but the log is a specific case. Option D is wrong because standardization does not change the shape of the distribution.

Answer

Box-Cox transformation with lambda=2.

Answer

Standardization (z-score).

Answer

Square root transformation.

MLS-C01 Exploratory Data Analysis Practice Test 17 — 15 Questions

Exhibit

MLS-C01 Exploratory Data Analysis Practice Test 17 — 15 Questions

Exhibit