MLS-C01 Mock Exam 96 — 25 Questions

Question 1

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for PostgreSQL and load it into Amazon Redshift. The Glue job runs nightly and takes 6 hours to complete. The Redshift cluster is a single dc2.large node. The team needs to reduce the load time to under 3 hours. The data volume is 200 GB per night. The team is considering using Amazon Redshift Spectrum to query data directly from S3 instead of loading it. However, the data transformation logic is complex and requires multiple joins and aggregations that are currently performed in Glue. Which approach should the team recommend to meet the time requirement?

Accepted Answer

Increase the Redshift cluster to a multi-node cluster with dc2.8xlarge nodes to improve COPY and query performance.. Option C is correct because increasing the node size or number of nodes provides more compute resources for the COPY command and any subsequent processing. Using a larger node type like dc2.8xlarge or adding nodes increases parallelism. Option A is wrong because Redshift Spectrum does not replace the transformation logic; the complex transformations would still need to be run, possibly in Glue or Redshift. Option B is wrong because staging data in S3 does not reduce the transformation time. Option D is wrong because using a single node with elastic resize is not possible for dc2; also, splitting the load does not reduce total time if the bottleneck is compute.

Answer

Use Redshift Spectrum to create external tables and run the transformations directly in Redshift, bypassing the Glue job.

Answer

Split the Glue job into multiple parallel jobs that each load a portion of the data into separate Redshift tables, then use UNION ALL views.

Answer

Stage the data in S3 in Parquet format and use a COPY command with the PARQUET option to load data faster.

Question 2

A company is building a data pipeline to process streaming data from IoT devices. The data is ingested via Amazon Kinesis Data Streams. Each record is about 1 KB. The company wants to use AWS Lambda for real-time transformations and then store the results in Amazon DynamoDB. The expected throughput is 10,000 records per second. The Lambda function currently runs in about 200 ms. The company is concerned about Lambda concurrency limits and wants to ensure there are no throttling errors. The default concurrency limit for Lambda is 1,000. Which approach should the team take to handle the expected throughput without throttling?

Accepted Answer

Increase the number of shards in the Kinesis Data Stream to 10 and set the Lambda batch size to 100.. Option B is correct because increasing the shard count to 10 ensures that each shard can trigger a Lambda invocation concurrently, and with a batch size of 100, the number of concurrent Lambda executions is at most 10 (10 shards * 1 batch per shard). This stays well within the concurrency limit. Option A is incorrect because reducing batch size increases the number of invocations per second (10,000 / 10 = 1,000 invocations per second), which would exceed the concurrency limit if each invocation takes 200 ms. Option C is incorrect because Kinesis Data Firehose does not support Lambda for per-record transformations. Option D is incorrect because increasing memory does not affect concurrency limits.

Answer

Increase the Lambda function memory to 3,000 MB to reduce the execution time below 100 ms.

Answer

Use Amazon Kinesis Data Firehose instead of Lambda to load data directly into DynamoDB.

Answer

Reduce the Lambda batch size to 10 so that each invocation processes fewer records, reducing the time per invocation.

Question 3

A data engineer is responsible for managing a data lake on Amazon S3. The data lake contains CSV files from various sources, totaling 10 TB. The engineer needs to make this data queryable using Amazon Athena. However, Athena queries are currently taking a long time and scanning large amounts of data. The engineer has noticed that the CSV files are not partitioned, and there are no indexes. The engineer wants to improve query performance and reduce costs. The data is accessed frequently for the last 30 days, but older data is rarely queried. The engineer also wants to minimize the amount of data scanned by Athena. What should the engineer do?

Accepted Answer

Convert the CSV files to Parquet format and partition the data by date.. Option D is correct. Converting CSV to Parquet reduces scan size due to columnar storage and compression. Partitioning by date allows Athena to skip irrelevant partitions. Option A is wrong because it does not address the partitioning issue. Option B is wrong because converting to ORC alone without partitioning helps but not as much as partitioning. Option C is wrong because Athena does not support indexes.

Answer

Convert the CSV files to JSON format and use Athena to query them.

Answer

Create indexes on the S3 objects using AWS Glue.

Answer

Convert the CSV files to ORC format and create a view in Athena.

Question 4

You are a data engineer at a fintech company. The company processes real-time stock market data from multiple exchanges. The data is ingested via Amazon Kinesis Data Streams with 50 shards. Each record is about 1 KB, and the ingestion rate is 5,000 records per second. The data is consumed by a Java application running on Amazon ECS that performs real-time analytics and stores results in Amazon DynamoDB. Recently, the application has been experiencing high latency, and some records are stuck in the shards for minutes before being consumed. The CloudWatch metrics show that the application's CPU utilization is low, but the iterator age is increasing. The application uses the Kinesis Client Library (KCL) with a single worker. What is the most likely cause and how should it be fixed?

Accepted Answer

Scale the number of KCL workers to match the number of shards (e.g., 50 workers) to process shards in parallel.. Option C is correct because a single KCL worker can only process one shard at a time; with 50 shards, records sit idle. Increasing the number of workers (e.g., to 50) allows parallel processing of all shards, reducing iterator age. Option A is wrong because the ingestion rate is well within the 50 shards' capacity (50 MB/s write vs ~5 MB/s actual). Option B is wrong because CPU is low, not high. Option D is wrong because DynamoDB is not part of the ingestion path.

Answer

Increase the number of shards to 200 to provide more throughput.

Answer

Increase the CPU capacity of the ECS task by moving to a larger instance type.

Answer

Move the destination from DynamoDB to Amazon RDS to reduce write latency.

Question 5

A data engineering team is building a real-time data pipeline using Amazon Kinesis Data Streams with AWS Lambda for processing. The pipeline ingests clickstream data from a mobile app. The team notices that occasionally, a Lambda function fails due to a transient error, and the failed record is not retried, leading to data loss. The Lambda function is configured with a batch size of 100 and a maximum retry count of 0. The team wants to ensure that all records are processed successfully, even if transient failures occur. They also want to minimize the impact of poison pill records that could block processing. Which combination of actions should the team take to address this issue?

Accepted Answer

Set the maximum retry count to 5, configure an on-failure destination Amazon SQS queue, and set up a dead-letter queue on that SQS queue for poison pills.. Option B is correct because increasing the maximum retry count allows Lambda to retry failed batches, and splitting the failure destination into a separate SQS queue with a dead-letter queue (DLQ) for poison pills ensures that problematic records are isolated and can be analyzed separately, while the main processing continues. Option A is incorrect because using a DLQ alone without retries will still lose records if retries are not enabled. Option C is incorrect because reducing batch size may help but does not solve the retry or poison pill problem. Option D is incorrect because Kinesis Data Firehose is not suitable for real-time per-record processing.

Answer

Set the maximum retry count to 5 and configure a dead-letter queue on the Lambda function to capture failed records after retries.

Answer

Switch to using Amazon Kinesis Data Firehose to buffer data and use AWS Lambda for transformation with built-in retry logic.

Answer

Reduce the batch size to 1 and increase the Lambda function timeout to handle transient errors.

Question 6

A machine learning team is using Amazon SageMaker to train models on a large dataset stored in Amazon S3. The dataset is 5 TB in size and is partitioned by date. The team wants to minimize data transfer costs and reduce training time by caching frequently accessed data locally on the training instances. The training instances are EC2 instances with attached Amazon EBS volumes. The team is considering using SageMaker Pipe mode to stream data directly from S3, but they are concerned about network bandwidth. Which approach should the team use to optimize data loading for training?

Accepted Answer

Use SageMaker File mode with Amazon EFS, which allows multiple training instances to share the same file system and caches data from S3.. Option D is correct because Amazon SageMaker File mode with Amazon Elastic File System (EFS) provides a shared file system that can cache data across training jobs, reducing the need to repeatedly download from S3. Option A is incorrect because FSx for Lustre is optimized for high-performance computing but not specifically for SageMaker training. Option B is incorrect because SageMaker Pipe mode streams data but does not cache. Option C is incorrect because EBS volumes are attached per instance and cannot be shared across jobs for caching.

Answer

Use Amazon FSx for Lustre as a high-performance file system linked to the S3 bucket, and mount it on the training instances.

Answer

Increase the size of the EBS volumes attached to the training instances and copy the entire dataset to the volumes before training.

Answer

Use SageMaker Pipe mode to stream data from S3 directly to the training algorithm, which automatically caches data in memory.

Question 7

A data engineer is setting up a data lake on Amazon S3 for a large retail company. The data includes customer transactions, inventory, and web logs. The company wants to use AWS Glue for ETL and Amazon Athena for ad-hoc queries. The data is partitioned by year, month, day, and hour. The engineer notices that Athena queries are slow and often scan large amounts of data even when only a specific hour is needed. The engineer has already enabled partitioning and used columnar formats like Parquet. What additional step should the engineer take to optimize query performance and reduce data scanned?

Accepted Answer

Use a coarser partition layout, such as partitioning only by date, and leverage Hive-style partitioning with AWS Glue Crawlers to avoid excessive small files.. Option C is correct because partitioning by hour alone can lead to many small files, which increases metadata overhead. Using a coarser partition like day and then using Hive-style partitioning with AWS Glue Crawlers will reduce the number of partitions and improve query performance. Option A is incorrect because S3 Select is for filtering within a single object, not for query optimization across multiple objects. Option B is incorrect because increasing the number of partitions further (e.g., adding minute) would worsen the small files problem. Option D is incorrect because converting to CSV would increase scan size and slow down queries.

Answer

Convert the Parquet files to CSV format to reduce the overhead of columnar storage and improve compression.

Answer

Use S3 Select to push down filters to S3, reducing the amount of data scanned by Athena.

Answer

Increase the granularity of partitioning to include minute-level partitions to further limit data scanned.

Question 8

A data science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. Which course of action is most appropriate?

Accepted Answer

Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns. Option B is correct because binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because log transformation does not reveal relationship with target. Option C is wrong because correlation is already computed and can mask non-linearity. Option D is wrong because feature importance from a tree model is more appropriate after feature engineering, not during EDA.

Answer

Conclude that 'transaction amount' is not predictive because the correlation is near zero

Answer

Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'

Answer

Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis

Question 9

A data engineer is performing EDA on a dataset containing user activity logs from a mobile app. The dataset has 10 million rows and includes columns: 'user_id', 'event_type', 'timestamp', 'device_type', and 'session_duration'. The engineer uses Amazon Athena to query the data stored in S3 as CSV files. The engineer runs a query to find the average session_duration per device_type, but the query takes over 5 minutes and scans 100 GB of data. The engineer wants to reduce query cost and improve performance for future EDA. The dataset is not partitioned, and the engineer anticipates frequent queries filtering on 'timestamp' and 'device_type'. Which action will most effectively reduce data scanned?

Accepted Answer

Partition the table by date derived from timestamp and convert to Parquet.. Option C is correct because partitioning by date (derived from timestamp) allows partition pruning when filtering by timestamp, significantly reducing data scanned. Converting to Parquet (Option A) helps but without partitioning, full scans still occur. Option B is wrong because it only partitions by device_type, but time-based filters are common. Option D is wrong because sampling loses accuracy.

Answer

Use random sampling to query a subset of data.

Answer

Convert the data to Parquet format and use columnar storage.

Answer

Partition the table by device_type.

Question 10

A data scientist is analyzing a dataset with a large number of missing values in several columns. The dataset is stored in an Amazon S3 bucket and is about 5 TB in size. The scientist wants to understand the pattern of missingness (e.g., is it missing completely at random, missing at random, or not missing at random) before deciding on an imputation strategy. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Studio. Which approach should the scientist take to best understand the missing data patterns?

Accepted Answer

Use AWS Glue DataBrew's data quality and missing data reports. AWS Glue DataBrew provides a missing data report that includes patterns and correlations of missingness, such as heatmaps and bar charts. This helps determine the type of missingness. Option B is wrong because SageMaker Data Wrangler does not have built-in missingness pattern analysis. Option C is wrong because Athena is for querying, not pattern analysis. Option D is wrong because Glue ETL jobs require custom coding and are less efficient for exploratory analysis.

Answer

Use Amazon SageMaker Data Wrangler to create a flow and analyze missingness visually

Answer

Use AWS Glue ETL jobs with PySpark to compute missingness statistics

Answer

Use Amazon Athena to run queries to find missing values per column

Question 11

A data scientist is working on a customer churn prediction project for a telecom company. The dataset contains 50,000 records with 25 features, including 'tenure' (number of months customer stayed), 'monthly_charges', 'total_charges', 'contract_type' (month-to-month, one year, two year), 'payment_method', and a target 'churn' (Yes/No). The data is stored in an S3 bucket as a single CSV file. The scientist uses Amazon SageMaker Data Wrangler to perform EDA. After importing the data, the scientist notices that the 'total_charges' column has many missing values (about 20% of rows). The scientist suspects that missing values occur only for customers with tenure = 0 (new customers). After verifying that suspicion, the scientist wants to handle the missing values appropriately. Which course of action should the scientist take?

Accepted Answer

Impute missing total_charges with 0, since missing values correspond to customers with tenure=0.. Option D is correct because if total_charges is missing only for tenure=0, it means those customers have not been billed yet, so total_charges should be 0. Imputing with 0 is appropriate. Option A is wrong because dropping rows with missing total_charges would remove all new customers, biasing the dataset. Option B is wrong because imputing with mean would assign incorrect values to new customers. Option C is wrong because using a model to predict missing values is overkill and may introduce error when the true value is known to be 0.

Answer

Use a regression model to predict total_charges based on other features.

Answer

Impute missing total_charges with the mean of non-missing values.

Answer

Drop all rows with missing total_charges to avoid bias.

Question 12

A data scientist is performing EDA on a dataset that contains customer demographics and purchase history. The dataset has a column 'age' with some values that are negative or unreasonably high (e.g., 200). The scientist wants to identify and handle these outliers. The scientist is using a SageMaker notebook with pandas. Which approach should the scientist take to effectively handle these outliers?

Accepted Answer

Define reasonable bounds based on domain knowledge and filter or cap the outliers. Using domain knowledge to define valid age range (e.g., 0-120) and filtering out or capping outliers is the most appropriate approach. Option B is wrong because removing the entire column loses information. Option C is wrong because imputing with mean distorts the distribution if outliers are extreme. Option D is wrong because standard scaling does not handle outliers; it will still be affected.

Answer

Apply standard scaling to the 'age' column

Answer

Impute the outlier values with the mean of the column

Answer

Remove the 'age' column entirely

Question 13

A data scientist is performing exploratory data analysis on a large dataset stored in Amazon S3 (100 GB, CSV format, 500 columns). The dataset contains customer transaction records with features such as transaction amount, timestamp, customer ID, and numerous categorical variables (e.g., product category, payment method, location). The scientist wants to understand the distribution of transaction amounts across different product categories and identify any outliers. They have an Amazon SageMaker notebook instance with a ml.t3.medium instance and are using pandas. However, when trying to load the entire dataset into a DataFrame using pd.read_csv('s3://bucket/data.csv'), the notebook crashes with a memory error. Additionally, the scientist suspects that some categorical columns have high cardinality (e.g., product category has thousands of unique values), and there are missing values in several columns. What is the MOST efficient approach to perform the EDA without modifying the original dataset or using additional AWS services? Options: A) Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib. B) Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories. C) Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA. D) Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.

Accepted Answer

Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories.. Option B is correct because it directly addresses the memory issue by processing data in chunks and handles high-cardinality categorical columns by focusing on top categories, all within the existing pandas environment without additional services. Option A requires PySpark which is not set up on the current instance and adds complexity. Option C, S3 Select, can reduce data size but cannot perform the aggregation needed (e.g., distribution across categories) without pulling all rows; it's more suitable for simple filtering. Option D, SageMaker Data Wrangler, is a separate service that requires additional setup and is not the most efficient for an ad-hoc EDA; it also modifies the workflow.

Answer

Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib.

Answer

Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA.

Answer

Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.

Question 14

A data scientist is analyzing a dataset of customer reviews for a retail company. The dataset contains text reviews, star ratings (1-5), and customer metadata. The scientist wants to perform sentiment analysis to classify reviews as positive or negative. During EDA, the scientist uses Amazon SageMaker Data Wrangler to visualize the distribution of star ratings and notices that 90% of reviews are 4 or 5 stars, while only 2% are 1 star. The scientist is concerned about class imbalance. Which approach should the scientist take to address the imbalance before modeling?

Accepted Answer

Use the F1-score as the evaluation metric to account for imbalance.. Option B is correct because using F1-score as the evaluation metric accounts for class imbalance better than accuracy. Option A is wrong because random oversampling can lead to overfitting and is not always best. Option C is wrong because downsampling the majority class loses valuable data. Option D is wrong because accuracy is misleading for imbalanced datasets.

Answer

Downsample the majority class to create a balanced dataset.

Answer

Use random oversampling of the minority class to balance the dataset.

Answer

Use accuracy as the evaluation metric since it is simple.

Question 15

A data scientist is working on a predictive maintenance project for a manufacturing company. Sensor data is collected every second from 100 machines and stored in an Amazon S3 bucket as Parquet files, partitioned by machine_id and date. The dataset is massive (10 TB) and contains over 2000 features per machine. The data scientist needs to perform exploratory data analysis to identify which features are most predictive of machine failure. They have access to Amazon SageMaker Studio with a SageMaker Data Wrangler flow. The initial data exploration is taking too long due to the volume of data. The data scientist wants to speed up the analysis without losing accuracy in feature selection. Which course of action is most appropriate?

Accepted Answer

Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample. Option B is correct because using SageMaker Data Wrangler's sampling capabilities allows faster exploration while preserving statistical properties for feature selection. Option A is wrong because reducing to a single partition loses time series context. Option C is wrong because moving to a smaller instance may cause memory issues. Option D is wrong because random sample of rows may break time series ordering.

Answer

Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset

Answer

Reduce the data to a single partition by concatenating all files and use only one machine's data

Answer

Use Amazon Athena to query a random sample of rows from the dataset

Question 16

A data scientist is working on a project to predict customer churn for a telecom company. The dataset includes 50,000 records with 20 features, including customer demographics, account information, and service usage. The data scientist uses Amazon SageMaker Studio and loads the data into a pandas DataFrame. During EDA, the data scientist notices that the target variable 'churn' has only 10% positive cases. Additionally, several features have missing values: 'income' has 5% missing, 'age' has 2% missing, and 'total_charges' has 1% missing. The data scientist also observes that 'income' is highly skewed with a long right tail, and 'age' is moderately skewed. The data scientist wants to handle missing values and prepare the data for modeling. Which course of action is most appropriate?

Accepted Answer

Impute 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.. Option A is correct because median imputation is robust for skewed data, and for the target imbalance, SMOTE can be applied after splitting. Option B is wrong because mean imputation is sensitive to skewness. Option C is wrong because removing rows with missing values would discard 8% of data, which is significant. Option D is wrong because mode is for categorical data, not continuous.

Answer

Remove all rows with any missing values, and use random oversampling to handle class imbalance.

Answer

Impute 'income' with mode, 'age' with mode, 'total_charges' with mode, and use SMOTE after splitting.

Answer

Impute all missing values with the mean of each column, and use stratified sampling to handle class imbalance.

Question 17

A data scientist is building a model to predict housing prices using a dataset with 100,000 records and 50 features. The features include 'sqft_living', 'sqft_lot', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', etc. The data scientist uses Amazon SageMaker Data Wrangler for EDA. Upon reviewing the data, the data scientist finds that 'sqft_living' has a correlation of 0.7 with 'sqft_above' (square footage above ground) and 0.6 with 'sqft_basement'. Also, 'grade' (overall grade of the house) is highly correlated with 'condition' (0.8). The target variable 'price' is right-skewed. The data scientist plans to use a linear regression model. Which set of actions should the data scientist take to improve model performance?

Accepted Answer

Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.. Option B is correct because log-transforming the target addresses skewness, and removing or combining highly correlated features reduces multicollinearity. Option A is wrong because removing all correlated features may discard predictive power. Option C is wrong because standard scaling does not fix skewness or multicollinearity. Option D is wrong because PCA on all features may lose interpretability and does not target the specific issues.

Answer

Apply standard scaling to all numeric features and use the data as is, since linear regression is robust to multicollinearity.

Answer

Remove all features that have correlation >0.5 with any other feature to eliminate multicollinearity, and apply standard scaling to all numeric features.

Answer

Apply principal component analysis (PCA) to all features to reduce dimensionality, and then fit linear regression on the principal components.

Question 18

A data scientist is analyzing a dataset of online retail transactions. The dataset contains 500,000 rows and 10 columns: 'TransactionID', 'CustomerID', 'ProductID', 'Quantity', 'UnitPrice', 'TransactionDate', 'PaymentMethod', 'ShippingAddress', 'Country', and 'TotalAmount'. The data scientist loads the data into a SageMaker notebook and performs initial EDA. The data scientist finds that 'UnitPrice' has a range from $0.01 to $10,000, with a mean of $50 and a median of $20. 'Quantity' ranges from -10 to 100, with negative values indicating returns. 'TotalAmount' is calculated as Quantity * UnitPrice. The data scientist also notices that 2% of the 'CustomerID' values are missing, and 1% of 'ProductID' values are missing. There are no missing values in other columns. The data scientist wants to clean the data and prepare it for customer segmentation. Which course of action is most appropriate?

Accepted Answer

Remove rows with negative 'Quantity' to focus on purchases. Impute missing 'CustomerID' and 'ProductID' with a placeholder such as 'Unknown'.. Option A is correct because negative quantities are returns and should be removed if the goal is to model purchase behavior, and missing CustomerID and ProductID can be imputed with 'Unknown' to avoid data loss. Option B is wrong because mean imputation for CustomerID is not valid (categorical). Option C is wrong because removing all rows with any missing values would discard 3% of data. Option D is wrong because negative quantities are meaningful as returns, not errors.

Answer

Impute missing 'CustomerID' with the mean of 'CustomerID' and missing 'ProductID' with the mode.

Answer

Remove all rows with any missing values.

Answer

Keep negative 'Quantity' and treat them as errors; replace them with the median of positive quantities.

Question 19

A data engineer is exploring a dataset with 1 million rows and 50 features. They notice that some features have missing values. The 'Age' column has 5% missingness, and 'Income' has 20% missingness. The target variable is 'LoanDefault' (binary). The engineer wants to impute missing values. Which of the following strategies is most appropriate?

Accepted Answer

Impute missing 'Age' with median and 'Income' with median.. Option B is correct because median imputation is robust to outliers and simple for numerical features. Option A is wrong because dropping rows with any missing values would remove 25% of data. Option C is wrong because mode is for categorical. Option D is wrong because model-based imputation is complex for initial EDA.

Answer

Impute missing 'Age' with mode and 'Income' with mode.

Answer

Use a k-NN model to predict missing values.

Answer

Drop all rows with missing values.

Question 20

A data scientist is analyzing a dataset with a binary target variable. They compute the correlation matrix and find that all features have correlations between -0.1 and 0.1 with the target. They suspect that the relationship might be non-linear. Which of the following techniques should they use to detect non-linear relationships?

Accepted Answer

Mutual information. Option C is correct because mutual information can capture any dependency. Option A is wrong because Pearson correlation only captures linear. Option B is wrong because Spearman captures monotonic but not all non-linear. Option D is wrong because ANOVA is for categorical vs continuous.

Answer

ANOVA test

Answer

Spearman's rank correlation

Answer

Pearson correlation coefficient