MLS-C01 Practice Exam 69 — 20 Questions

Question 1

A data scientist is exploring a dataset with 100 features. The goal is to build a binary classification model. The dataset is highly imbalanced with 95% negative class and 5% positive class. The data scientist wants to understand the relationship between features and the target. Which technique is most appropriate for initial exploratory analysis?

Accepted Answer

Use stratified sampling to create a balanced subset for visualization and correlation analysis.. Option A is correct because stratified sampling ensures that the minority class is adequately represented in the sample. Option B is wrong because random sampling may miss the rare class entirely. Option C is wrong because SMOTE is a data augmentation technique for training, not for EDA. Option D is wrong because removing the minority class would prevent analyzing the target.

Answer

Remove the minority class samples and analyze the majority class only.

Answer

Use random sampling to select 10% of the data for EDA.

Answer

Apply SMOTE to the dataset before performing EDA.

Question 2

A data engineer is performing EDA on a time-series dataset of server metrics (CPU, memory, disk I/O) collected every minute. The dataset contains 2 years of data. The engineer suspects there are seasonal patterns and wants to decompose the time series for one metric. Which AWS service can be used to perform this decomposition natively?

Accepted Answer

Amazon SageMaker Canvas. Option A is correct because Amazon SageMaker Canvas supports time-series forecasting with built-in decomposition. Option B is wrong because AWS Glue does not have time-series decomposition. Option C is wrong because Amazon QuickSight does not decompose time series. Option D is wrong because Amazon Athena does not have decomposition functions.

Answer

Amazon Athena with SQL window functions

Answer

Amazon QuickSight

Answer

AWS Glue DataBrew

Question 3

A data scientist runs a SQL query on an Amazon Athena table and notices that the query scans a large amount of data. Which approach would reduce the amount of data scanned without changing the SQL logic?

Accepted Answer

Partition the table on a column that is frequently used in WHERE clauses.. Partitioning the table on a frequently filtered column limits the data scanned to relevant partitions. Option A is wrong because compressing reduces storage but not scan size unless combined with columnar format. Option C is wrong because converting to JSON does not reduce scan. Option D is wrong because Parquet is columnar and can reduce scan, but without partitioning, Athena still scans entire columns.

Answer

Convert the data from CSV to JSON format.

Answer

Store the data in Parquet format without partitioning.

Answer

Use GZIP compression on the data files.

Question 4

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 5

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.

Question 6

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. Option A (Amazon QuickSight) is a BI tool for dashboards, not embedded data wrangling. Option C (AWS Glue) is for ETL jobs. Option D (Amazon Athena) is for querying data in S3.

Answer

Amazon QuickSight

Answer

Amazon Athena

Answer

AWS Glue DataBrew

Question 7

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 D (Linear regression) is not for outlier detection. Option E (K-means) can be used for clustering but not primarily for outlier detection.

Answer

Linear regression residuals.

Answer

K-means clustering.

Question 8

A data scientist is using Amazon SageMaker Studio notebooks for EDA. They want to share a reproducible report that includes code, visualizations, and narrative text with their team. Which approach should they use?

Accepted Answer

Save the notebook as an .ipynb file and share it via Amazon S3.. Option C is correct because a Jupyter notebook (in Studio) contains code and markdown and can be shared. Option A (QuickSight dashboard) is for interactive dashboards; Option B (SageMaker Autopilot) is for automated ML; Option D (SageMaker Clarify) is for bias detection.

Answer

Use Amazon SageMaker Clarify to generate an EDA report.

Answer

Export the results to Amazon QuickSight and create a dashboard.

Answer

Use Amazon SageMaker Autopilot to generate a report.

Question 9

A company is storing customer transaction data in Amazon S3 as CSV files. A data scientist uses AWS Glue to crawl the data and create a table in the AWS Glue Data Catalog. When querying the table with Amazon Athena, the data scientist notices that some columns have NULL values where data should exist. The data scientist examines the raw CSV files and confirms the data is present. What is the most likely cause of the NULL values?

Accepted Answer

The CSV files have different schemas (e.g., different columns) across partitions.. Option D is correct because the Glue crawler infers schema from the first few files; if later files have different schemas (e.g., more columns), the extra data is not captured. Option A is wrong because the crawler handles CSV without SerDe issues. Option B is wrong because Athena does not modify data. Option C is wrong because the issue is schema mismatch, not data type inference.

Answer

Athena is configured to skip corrupted records, causing NULLs.

Answer

The Glue crawler incorrectly inferred the data type of the columns.

Answer

The CSV files use a custom delimiter that the Glue crawler does not recognize.

Question 10

A data scientist is granted the IAM policy shown in the exhibit. The data scientist can query the 'data-lake-bucket' using Athena and get results. However, when the data scientist tries to run a CTAS (CREATE TABLE AS SELECT) query in Athena to write results to a new S3 location, the query fails. What is the most likely reason?

Accepted Answer

The policy does not grant s3:PutObject permission on the bucket.. Option B is correct because the policy allows s3:GetObject and s3:ListBucket, but not s3:PutObject, which is required for CTAS queries. Option A is wrong because the policy uses resource-level permissions for S3. Option C is wrong because Athena does not require Glue Data Catalog permissions for CTAS if the table metadata is already stored. Option D is wrong because the policy does not restrict Athena resource ARNs.

Answer

The policy does not grant athena:CreateTable permission.

Answer

The policy does not grant permissions to the Glue Data Catalog.

Answer

The policy uses a wildcard for Athena actions, which is not allowed.

Question 11

A data scientist is exploring a dataset with 100 features. After generating pair plots, the scientist notices that many features have skewed distributions. Which transformation should the scientist apply to make the distributions more Gaussian-like for modeling?

Accepted Answer

Yeo-Johnson transformation. Option C is correct because Yeo-Johnson can handle both positive and negative values. Option A is wrong because log transform only works for positive values. Option B is wrong because Box-Cox also requires positive values. Option D is wrong because standard scaling does not fix skewness.

Answer

Log transformation

Answer

Standard scaling (z-score normalization)

Answer

Box-Cox transformation

Question 12

A data engineer ingests streaming data into Amazon Kinesis Data Streams. The data science team needs to analyze the data using Amazon SageMaker notebooks. What is the most efficient way to provide access to the stream data for ad-hoc exploration?

Accepted Answer

Use the Kinesis connector for Spark to read data directly from the stream into a Spark DataFrame in the notebook.. Using the Kinesis connector for Spark in a SageMaker notebook allows reading from the stream directly. Option A is wrong because S3 ingestion adds latency and additional steps. Option B is wrong because Kinesis Agent is for data producers, not consumers. Option D is wrong because Lambda transformation is not needed for exploration.

Answer

Create an AWS Lambda function to transform and write data to DynamoDB, then query DynamoDB from the notebook.

Answer

Configure a Kinesis Firehose delivery stream to deliver data to an S3 bucket, then query the data from the notebook using Athena.

Answer

Install the Kinesis Agent on the SageMaker notebook instance and configure it to write data to a local file.

Question 13

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 14

A data scientist is performing exploratory data analysis on a dataset with both numerical and categorical features. The scientist wants to visualize the pairwise relationships between numerical features and also see the distribution of each feature. Which type of plot should the scientist use?

Accepted Answer

Pair plot (scatter matrix) with histograms on the diagonal.. Option C is correct because a pair plot (scatter matrix) shows pairwise scatter plots and histograms on the diagonal. Option A is wrong because a correlation matrix does not show distributions. Option B is wrong because a heatmap only shows correlation values. Option D is wrong because a box plot does not show pairwise relationships.

Answer

Box plot for each feature.

Answer

Heatmap of the correlation matrix.

Answer

Correlation matrix with numbers.

Question 15

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.. Using Amazon SageMaker Data Wrangler to generate histograms segmented by class is a straightforward way to visualize feature distributions for each class. Option B (t-tests) may be used later but doesn't provide distribution visualization. Option C (box plots) is a good alternative but not as comprehensive as histograms for distribution shape. Option D (correlation matrix) does not show class-wise distribution.

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 16

Which TWO of the following are best practices for exploratory data analysis when using Amazon SageMaker Data Wrangler? (Select TWO.)

Accepted Answer

Use Data Wrangler's built-in data visualizations to explore feature distributions and relationships.. Using Data Wrangler's built-in visualizations for quick analysis and exporting the flow as a Jupyter notebook for reproducibility are best practices. Option B (QuickSight) is separate. Option C (EMR) is not needed. Option D (Athena) is for queries, not for building into pipeline.

Answer

Store all intermediate results in Amazon Athena for querying.

Answer

Use Amazon EMR to run Spark jobs for data profiling.

Answer

Always export the data to Amazon QuickSight for analysis before transformation.

Question 17

Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)

Accepted Answer

Impute with the mode (most frequent) of the column.. Options B and D are correct. B: Creating an 'Unknown' category preserves information about missingness. D: Using mode imputation is a simple baseline. A: Dropping rows may lose data; C: Mean is for numerical data; E: One-hot encoding requires values first.

Answer

Use one-hot encoding to represent missingness as a binary feature.

Answer

Drop all rows with missing values in that column.

Answer

Impute missing values with the mean of the column.

Question 18

A data scientist is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a dataset. The dataset contains a feature 'age' with values ranging from 0 to 120. The data scientist wants to detect outliers. Which built-in transform in Data Wrangler is most appropriate for this task?

Accepted Answer

Handle Outliers. Option C is correct because the 'Handle Outliers' transform provides methods like IQR and z-score to detect and handle outliers. Option A is wrong because 'Handle Missing' deals with missing values, not outliers. Option B is wrong because 'Scale and Normalize' transforms data but does not detect outliers. Option D is wrong because 'Encode Categorical' is for categorical features.

Answer

Scale and Normalize

Answer

Handle Missing

Answer

Encode Categorical

Question 19

A data scientist is investigating an application that logs errors to Amazon CloudWatch Logs. The data scientist runs the CloudWatch Logs Insights query shown in the exhibit. The query returns no results, even though the data scientist knows errors have occurred. What is the most likely cause?

Accepted Answer

The filter pattern is case-sensitive and the log messages use a different case for 'error'.. Option A is correct because the query is case-sensitive; 'ERROR' may not match 'error' or 'Error'. Option B is wrong because the sort order does not affect whether results are returned. Option C is wrong because the query uses correct syntax. Option D is wrong because bin(5m) is valid if there are logs within the time range.

Answer

The stats count() function is misspelled.

Answer

The query sorts by timestamp descending, which hides results.

Answer

The bin(5m) function is not supported in CloudWatch Logs Insights.

Question 20

A data scientist is performing EDA on a dataset with 500,000 rows and 10 columns. The dataset is stored in an S3 bucket as CSV files. The scientist wants to generate summary statistics (mean, median, min, max) for all numeric columns. Which service allows the quickest ad-hoc analysis without provisioning any infrastructure?

Accepted Answer

Amazon Athena. Option B is correct because Amazon Athena can query data in S3 directly using SQL. Option A is wrong because SageMaker Data Wrangler requires a notebook instance. Option C is wrong because AWS Glue ETL requires job setup. Option D is wrong because QuickSight is for visualization, not direct summary statistics.

Answer

AWS Glue ETL

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight