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

Question 1

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?

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

Accepted Answer

L1-regularized logistic regression. Options C and D are correct. L1-regularized logistic regression (option C) drives coefficients to zero for irrelevant features, effectively performing feature selection. Mutual information-based feature selection (option D) measures dependency between each feature and the target, selecting features with highest mutual information. Option A (PCA) is unsupervised and may discard target-related variance. Option B (Autoencoders) is unsupervised and not directly target-aware. Option E (t-SNE) is for visualization, not feature selection.

Answer

Principal Component Analysis (PCA)

Answer

Autoencoders

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Question 2

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, C, 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 B (AWS Data Pipeline) is wrong because it is for workflow orchestration, not EDA. Option E (Amazon Kinesis Data Analytics) is wrong because it is for streaming data, not batch EDA.

Answer

AWS Data Pipeline

Answer

Amazon Kinesis Data Analytics

Question 3

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.. Different numbers of rows directly affect file size. Options A and B are incorrect: compression algorithms would cause size differences but the files are CSV and likely uncompressed; duplicate headers would cause schema inconsistency, but the question states same number of columns. Option D is incorrect because different column data types do not necessarily cause size variation; they could still have same number of rows.

Answer

The files are compressed with different algorithms.

Answer

Some files have duplicate headers.

Answer

The files have different column data types.

Question 4

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.. The IAM policy likely includes a Deny statement for the 'sensitive/' prefix, causing access denied when Athena attempts to read data from that location. Option A is incorrect because s3:ListBucket is typically required and allowed; the error is access denied rather than a forbidden error. Option B is incorrect because a syntax error would usually produce an invalid policy error, not an access denied error during query execution. Option D is incorrect because if s3:GetObject were explicitly denied for all objects, the error would occur for all queries, but the issue is specific to the 'sensitive/' prefix.

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 5

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.. Modifying the query to include the @message field for the specific hour with the spike allows the engineer to see the actual error messages, which is the direct way to identify the root cause. Option B is incorrect because removing the filter would show all messages, which is less targeted. Option C is incorrect because changing the bin to 5m might be too granular and not helpful without examining the messages. Option D is incorrect because increasing the limit does not help identify the cause without seeing the messages.

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 6

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 column that is frequently used in WHERE clauses allows Athena to prune partitions and only scan the relevant data, reducing the amount of data scanned. Option B (JSON) does not reduce scan because it is not columnar. Option C (Parquet without partitioning) is columnar and can reduce scan through column pruning, but without partitioning it still scans entire columns. Option D (GZIP) compresses data but Athena decompresses and scans the full file size, so no reduction in scanned data.

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 7

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.. The Kinesis connector for Spark enables direct reading of streaming data into a Spark DataFrame in the SageMaker notebook, allowing real-time ad-hoc analysis with minimal latency. Option A is incorrect because writing to DynamoDB via Lambda adds unnecessary transformation steps and latency, and DynamoDB is not optimized for large-scale streaming data exploration. Option B is incorrect because using Kinesis Firehose to deliver data to S3 and then querying with Athena introduces significant latency (data is written in batches) and is not suitable for real-time exploration. Option C is incorrect because the Kinesis Agent is designed for sending data from sources to Kinesis, not for consuming or reading data; it cannot be used to read stream data into a notebook.

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 8

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 is crucial for highly imbalanced datasets to ensure that the rare class is proportionally represented in both training and testing splits, allowing for proper evaluation. Normalization (A) is important but does not address imbalance. Removing outliers (B) could remove fraud cases. Correlation analysis (D) is useful but not the most critical step for imbalance.

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 9

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 A (PCA) is for dimensionality reduction, not initial EDA. Option B (gradient boosting) is modeling, not EDA. Option E (one-hot encoding) is for categorical variables, but not an EDA action. The correct actions are C and D.

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 10

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).. The appropriate EDA steps for an image dataset include analyzing image dimensions (A) to understand size variability and potential resizing needs, checking for corrupted or unreadable files (B) to ensure data integrity, and visualizing sample images per class (D) to verify label accuracy and detect labeling errors. Option C (time series decomposition) is irrelevant because timestamps, while possibly present, are not a primary focus of standard image EDA; it would be relevant for time-series data. Option E (tokenization and stop word removal) applies to text data, not images.

Answer

Decompose the time series of image timestamps to detect seasonality.

Answer

Perform tokenization and stop word removal on image filenames.

Question 11

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 require permissions to write query results to an S3 bucket, typically via 's3:PutObject' on an output location. The policy only allows 's3:GetObject' and 's3:ListBucket' on the data bucket, but lacks any 's3:PutObject' permission, causing the failure. Option A is incorrect because the policy does not reference any KMS actions. Option B is incorrect because 'athena:StartQueryExecution' is implicitly allowed (not denied), though it doesn't appear in the policy; Athena actions are not shown but the failure is due to S3 write permissions. Option C is incorrect because 's3:GetObject' is explicitly allowed on the bucket.

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 12

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.. Creating an Athena table with the LOCATION pointing to 's3://my-bucket/logs/2023/' will include all objects under that prefix, regardless of size. The CLI command's --query parameter only filters the output of the list-objects command, but does not impose any restriction on the data or the bucket. Option B is incorrect because a WHERE clause in Athena can only filter rows after the table is defined; it cannot include or exclude files from being read. Option C is incorrect because S3 does not have a built-in size restriction on a bucket; the CLI command is just a client-side query. Option D is incorrect because the CLI command is independent; the table can be created directly without needing to modify the CLI command.

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 13

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 errors indicate that the Glue ETL job is exceeding its configured timeout. To diagnose the root cause, the data scientist should check the source database table sizes and row counts over time (option B). This helps determine if the data volume has increased, causing longer processing times. Option A (testing network connectivity) addresses network issues but not processing delays. Option C (switching job type) may change performance but does not diagnose the cause. Option D (increasing timeout) is a temporary workaround, not a diagnostic step.

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 14

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 (C) are appropriate for visualizing relationships between numerical features and a continuous target. Box plots (D) are appropriate for comparing target distribution across categories of categorical features. Option A (confusion matrix) is used for classification, not regression. Option B (silhouette score) is for evaluating clustering. Option E (histogram of target) is univariate and does not show feature-target relationships.

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 15

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).. The three correct considerations are: missing data mechanism (MCAR/MAR/MNAR) which determines whether imputation can be unbiased; percentage of missing values in each feature, which affects the reliability of imputation and whether deletion is preferable; and feature distribution (e.g., skewed, normal), which guides the choice between mean, median, or model-based imputation. Option B (class imbalance) is a consideration for classification models, not imputation. Option E (feature importance) is not a standard criterion for choosing imputation methods.

Answer

The class imbalance of the target variable.

Answer

The feature importance according to a random forest model.