MLS-C01 Exploratory Data Analysis Practice Test 16 — 15 Questions

Question 1

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 2

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

Question 3

A data scientist is analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method is MOST appropriate?

Accepted Answer

Multiple imputation by chained equations (MICE). Option D is correct because MICE uses multiple imputation based on other features, accounting for correlations. Option A is wrong because mean imputation ignores correlation. Option B is wrong because median imputation also ignores correlation. Option C is wrong because removing rows loses data and may introduce bias.

Answer

Median imputation

Answer

Listwise deletion (remove rows with missing values)

Answer

Mean imputation

Question 4

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target is binary. The scientist wants to reduce dimensionality while preserving information related to the target. Which TWO methods are appropriate?

Accepted Answer

L1-regularized logistic regression. Options A and D are correct. Mutual information selection selects features with highest dependency on target, and L1-regularized logistic regression can drive coefficients to zero for feature selection. Option B is wrong because PCA is unsupervised and may discard target-related variance. Option C is wrong because t-SNE is for visualization only. Option E is wrong because Autoencoders are unsupervised.

Answer

Principal Component Analysis (PCA)

Answer

Autoencoders

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Question 5

A data scientist is exploring a dataset with mixed data types (numeric, categorical, text). The dataset has 5 million rows. The scientist wants to understand the relationships between variables and identify potential data quality issues. Which THREE tools are suitable for this analysis?

Accepted Answer

AWS Glue DataBrew. Options A, B, and D are correct. AWS Glue DataBrew can profile data, visualize distributions, and detect anomalies. Amazon SageMaker Data Wrangler provides interactive data preparation and visualization. Amazon Athena can be used to run SQL queries for data quality checks. Option C is wrong because AWS Data Pipeline is for workflow orchestration, not EDA. Option E is wrong because Amazon Kinesis Data Analytics is for streaming data, not batch EDA.

Answer

AWS Data Pipeline

Answer

Amazon Kinesis Data Analytics

Question 6

A data scientist is working with a dataset that contains geolocation coordinates (latitude and longitude) and timestamps. The scientist wants to visualize the data to check for spatial and temporal patterns. Which TWO AWS services can be used for this visualization?

Accepted Answer

Amazon SageMaker Data Wrangler. Options B and D are correct. Amazon QuickSight supports geospatial charts and time-series. Amazon SageMaker Data Wrangler can also visualize geospatial data as scatter plots. Option A is wrong because Amazon Comprehend is for NLP. Option C is wrong because AWS Glue is for ETL. Option E is wrong because Amazon Rekognition is for image/video analysis.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

AWS Glue

Question 7

An ML engineer runs the AWS CLI command above to list files in a training data bucket. The engineer notices that the three CSV files have different sizes but the same number of columns. What is the MOST likely cause of the size variation?

Accepted Answer

The files contain a different number of rows.. Option D is correct because the number of rows can vary between files, leading to different file sizes. Option A is wrong because different column types would cause inconsistent schemas, but the engineer says same number of columns. Option B is wrong because compression would be applied uniformly. Option C is wrong because S3 does not add headers multiple times.

Answer

The files are compressed with different algorithms.

Answer

Some files have duplicate headers.

Answer

The files have different column data types.

Question 8

An IAM policy is attached to a data scientist's role. The scientist is trying to list objects in the 'data-bucket' using Amazon Athena. The query fails with an access denied error. What is the MOST likely reason?

Accepted Answer

The query is trying to read data from the 'sensitive/' prefix.. Option B is correct because Athena needs s3:GetObject on the bucket to read data, but the Deny statement prevents access to objects under 'sensitive/' prefix. However, the query may be trying to read from that prefix. Option A is wrong because ListBucket is allowed. Option C is wrong because Deny blocks GetObject. Option D is wrong because the policy is valid.

Answer

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

Answer

The policy has a syntax error.

Answer

The s3:GetObject action is explicitly denied for all objects.

Question 9

A DevOps engineer runs the CloudWatch Logs Insights query shown above on the log group for an ML training job. The result shows a spike in ERROR messages at a specific hour. What should the engineer do next to identify the root cause?

Accepted Answer

Modify the query to display the actual @message for the hour with the spike.. Option C is correct because examining the actual error messages during that hour can reveal the cause. Option A is wrong because bin(5m) may be too granular. Option B is wrong because the query already uses 1h bins. Option D is wrong because the spike is already identified.

Answer

Remove the filter on ERROR to see all messages.

Answer

Change the bin to 5m to see more detailed spikes.

Answer

Increase the limit to 50 to see more hours.

Question 10

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 11

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 12

A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?

Accepted Answer

Create a stratified train-test split to preserve the class distribution.. Stratified sampling ensures the rare class is represented in train/test splits, preserving the imbalanced ratio for evaluation. Option A is wrong because normalization does not address imbalance. Option C is wrong because correlation analysis is not specific to imbalance. Option D is wrong because removing outliers could eliminate fraud cases.

Answer

Normalize all numerical features to have zero mean and unit variance.

Answer

Remove outliers from the dataset using the IQR method.

Answer

Perform correlation analysis to remove highly correlated features.

Question 13

A data scientist is exploring a dataset containing customer transaction records. The target variable is 'churn' (1 = churned, 0 = not churned). Which TWO actions should the scientist take to understand the data distribution and prepare for modeling?

Accepted Answer

Plot the frequency of the target variable to check for class imbalance.. Visualizing class imbalance and identifying missing values are fundamental EDA steps. Option B (PCA) is for dimensionality reduction, not initial EDA. Option D (one-hot encoding) is for categorical variables, but not an EDA action. Option E (gradient boosting) is modeling, not EDA.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Train a gradient boosting model to identify important features.

Answer

Convert categorical variables into one-hot encoded vectors.

Question 14

A data scientist is analyzing a large dataset of images stored in Amazon S3. The dataset is used to train a computer vision model. Which THREE EDA steps are appropriate for this image dataset?

Accepted Answer

Compute the distribution of image dimensions (height and width).. Checking label distribution, identifying corrupt images, and analyzing image dimensions are standard EDA for images. Option B (time series decomposition) is irrelevant. Option E (text preprocessing) is for text data.

Answer

Decompose the time series of image timestamps to detect seasonality.

Answer

Perform tokenization and stop word removal on image filenames.

Question 15

A data analyst is performing EDA on a tabular dataset with 500 features. The goal is to reduce dimensionality before modeling. Which TWO techniques are appropriate for this task?

Accepted Answer

t-distributed Stochastic Neighbor Embedding (t-SNE).. PCA and t-SNE are both dimensionality reduction techniques. PCA is linear, t-SNE is nonlinear. Option A (Chi-square test) is for feature selection with categorical targets, not dimensionality reduction. Option C (cross-validation) is for model evaluation. Option E (one-hot encoding) expands features.

Answer

k-fold cross-validation to assess model performance.

Answer

Chi-square test for independence between features.

Answer

One-hot encoding of categorical variables.