MLA-C01 Data Preparation for Machine Learning — 20 Questions

Question 1

A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?

Accepted Answer

Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.. Amazon SageMaker Data Wrangler provides a visual interface and built-in transforms for handling missing values efficiently at scale, without writing custom code. Glue ETL is more code-heavy, and imputation with pandas is not scalable for large datasets. Removing all rows with missing values is not always optimal and may not be efficient.

Answer

Use AWS Glue ETL to write a custom Python script that imputes missing values with the mean.

Answer

Use pandas in a SageMaker notebook to impute missing values with the median.

Answer

Remove all rows with missing values from the dataset.

Question 2

A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?

Accepted Answer

Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.. Option B is correct because AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.

Answer

Use Amazon Athena to convert the data to JSON format and store it in S3.

Answer

Use AWS Glue to convert the data to Apache Hive format.

Answer

Use Apache Spark DataFrame to write the data as CSV with Snappy compression.

Question 3

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?

Accepted Answer

Apply Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.. Option B is correct because SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the severe class imbalance (95% class A, 5% class B) by creating a more balanced training dataset. This technique is particularly effective for tabular data in Amazon SageMaker, as it increases the representation of the minority class without simply duplicating existing samples, thereby reducing overfitting and improving the model's ability to learn decision boundaries for the minority class.

Answer

Apply data augmentation to the majority class by adding noise.

Answer

Use a weighted loss function during training to penalize misclassifications of the minority class.

Answer

Apply random under-sampling to reduce the majority class to match the minority class size.

Question 4

A data scientist is preparing a dataset for a machine learning model that predicts customer churn. The dataset contains a column 'CustomerID' that is a unique identifier. What should the data scientist do with this column before training the model?

Accepted Answer

Remove the column from the feature set.. Option C is correct because 'CustomerID' is a unique identifier with no predictive power for churn. Including it as a feature would cause the model to memorize individual customers rather than learn generalizable patterns, leading to overfitting and poor performance on unseen data. In machine learning, such columns should be removed during data preparation to ensure the model learns from meaningful features.

Answer

Keep the column as a feature because it uniquely identifies each customer.

Answer

Use the column as the target variable.

Answer

Encode the column using one-hot encoding.

Question 5

A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The data is stored in Amazon S3 in Parquet format. A data engineer notices that the Glue job is running slowly and consuming a lot of resources. What is the MOST cost-effective way to improve the performance of the Glue job?

Accepted Answer

Use the G.1X worker type, which provides more memory per worker compared to the Standard worker type.. Increasing the number of DPUs (Data Processing Units) in AWS Glue can improve parallelism and reduce job runtime, but it increases cost. Using G.1X worker type with more memory per worker can improve performance without increasing DPU count, offering better resource utilization. Switching to CSV may degrade performance. Using partition pruning on the source data can reduce data scanned but may not address resource consumption.

Answer

Use partition pruning on the source data to reduce the amount of data processed.

Answer

Switch the output format from Parquet to CSV to reduce processing overhead.

Answer

Use a larger instance type for the Glue job by increasing the number of DPUs.

Question 6

A machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?

Accepted Answer

Apply target encoding (mean encoding) to the high-cardinality features.. For high-cardinality categorical features, target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which captures information without creating a large number of dummy variables. One-hot encoding would create too many features. Label encoding implies ordinal relationships. Hash encoding can cause collisions.

Answer

Apply hash encoding to map categories to a fixed number of buckets.

Answer

Apply one-hot encoding to all categorical features.

Answer

Apply label encoding to assign integer values to each category.

Question 7

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column with date strings in the format 'YYYY-MM-DD'. The data scientist wants to extract the year, month, and day as separate features. Which Data Wrangler transform should be used?

Accepted Answer

Parse date transform.. The 'Parse date' transform in Amazon SageMaker Data Wrangler is specifically designed to convert date strings into structured datetime components. By applying this transform to the 'YYYY-MM-DD' column, the data scientist can automatically extract year, month, and day as separate features, enabling downstream feature engineering without manual string parsing.

Answer

Encode categorical transform.

Answer

Scale values transform.

Answer

Handle missing transform.

Question 8

A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)

Accepted Answer

Use AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.. Option D is correct because AWS Glue DynamicFrames, built on Apache Spark, provide a scalable, distributed computing environment to compute statistical measures like the interquartile range (IQR) across large datasets. This allows the engineer to programmatically filter outliers without manual intervention, leveraging Spark's parallel processing for efficient handling of data at scale.

Answer

Manually remove outliers by inspecting the data in Amazon S3.

Answer

Train a neural network to identify anomalies and remove them.

Answer

Use pandas in a SageMaker notebook to calculate z-scores and filter outliers.

Question 9

A machine learning team is preparing a dataset for a regression model. The dataset contains numerical features that are on different scales (e.g., age 0-100, income 0-1,000,000). The team plans to use Amazon SageMaker to train a linear regression model. Which THREE data preparation steps should the team take to ensure the model performs well? (Select THREE.)

Accepted Answer

Handle missing values by imputation or removal.. Option C is correct because missing values can cause errors or biased estimates in linear regression models. Amazon SageMaker's built-in linear regression algorithm does not handle missing data automatically, so imputation (e.g., mean/median) or removal is necessary to ensure the training process completes and produces reliable coefficients.

Answer

Apply feature selection to reduce the number of features.

Answer

Remove outliers from the dataset.

Question 10

A data scientist is using Amazon SageMaker Data Wrangler to create a data flow for a machine learning project. The source data is in Amazon S3 and contains PII (personally identifiable information) such as email addresses and credit card numbers. The data scientist needs to prepare the data for training while ensuring compliance with data privacy regulations. Which THREE actions should the data scientist take? (Select THREE.)

Accepted Answer

Use Data Wrangler to redact or remove PII columns from the dataset before training.. Option B is correct because Amazon SageMaker Data Wrangler provides built-in transforms to redact or remove PII columns, which directly addresses compliance requirements by eliminating sensitive data from the training dataset. This is a straightforward and effective method to prevent PII from being used in model training, reducing the risk of data exposure.

Answer

Include the raw PII in the training dataset and rely on the model to not memorize it.

Answer

Use AWS Glue to copy the data to a separate bucket without any transformations.

Question 11

A data scientist is preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique). SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.

Answer

Random undersampling of the majority class

Answer

Random oversampling of the minority class

Answer

Apply class weights during model training

Question 12

A machine learning engineer is preparing a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). Which technique is most appropriate for encoding these high-cardinality categorical features?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, effectively capturing the predictive signal while keeping the feature as a single numeric column. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordinality of label encoding, making it a common choice in gradient boosting frameworks like XGBoost or LightGBM for datasets with thousands of unique categories.

Answer

Label encoding

Answer

One-hot encoding

Answer

Frequency encoding

Question 13

A team is building a regression model on a dataset with missing values in multiple features. They decide to use a k-Nearest Neighbors (k-NN) imputer. The dataset has 100,000 rows and 50 features. Which step should the team take to ensure the imputation is efficient and accurate?

Accepted Answer

Standardize the features before applying k-NN imputation. Standardizing features before applying k-NN imputation is critical because k-NN relies on distance calculations (e.g., Euclidean distance). If features are on different scales (e.g., one feature ranges 0–1 and another 0–100,000), the distance metric will be dominated by the larger-scale feature, leading to biased neighbor selection and inaccurate imputation. Standardization (e.g., z-score scaling) ensures each feature contributes equally to the distance computation, improving both efficiency and accuracy.

Answer

Set k=1 to minimize bias

Answer

Use all 100,000 rows to find neighbors for each missing value

Answer

Use only the feature with missing values to find neighbors

Question 14

A data engineer is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column 'review_date' with timestamps. The engineer wants to extract the day of the week as a new feature. How should this transformation be performed in Data Wrangler?

Accepted Answer

Use the 'day_of_week' transform on the 'review_date' column. Option D is correct because Amazon SageMaker Data Wrangler includes a built-in 'day_of_week' transform that directly extracts the day of the week (e.g., Monday, Tuesday) from a timestamp column without requiring custom code or additional formatting. This transform is optimized for Data Wrangler's visual interface and integrates seamlessly with its processing pipeline.

Answer

Write a custom Python script using pandas dt.day_name()

Answer

Use one-hot encoding on the timestamp

Answer

Use the 'extract' transform with format '%A'

Question 15

A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?

Accepted Answer

Log transformation (natural log). A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.

Answer

Standardization (z-score)

Answer

Min-max scaling to [0,1]

Answer

Equal-width binning

Question 16

A data scientist is working on a time series forecasting problem. The dataset contains a column 'sales' with occasional negative values due to returns. The model expects non-negative input. Which data preparation step should be taken?

Accepted Answer

Clip negative sales values to zero. Option A is correct because clipping negative sales values to zero directly addresses the model's requirement for non-negative input while preserving the data's temporal structure. This approach is appropriate for time series forecasting where returns cause occasional negative values, as it treats returns as zero sales rather than removing or distorting the data points.

Answer

Apply log transformation after adding a constant

Answer

Remove all rows with negative sales values

Answer

Impute negative values with the mean

Question 17

A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

Accepted Answer

Read the Parquet files directly using SparkSession.read.parquet. Option D is correct because SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

Answer

Read the Parquet files as text using SparkContext.textFile

Answer

Split the dataset into many small Parquet files (e.g., 1 MB each)

Answer

Convert the Parquet files to CSV before processing

Question 18

A machine learning engineer is preparing a dataset for a multiclass classification task. The dataset has 10 features and 100,000 rows. Which TWO techniques should the engineer use to reduce the risk of overfitting during data preparation?

Accepted Answer

Data augmentation (e.g., adding noise). Data augmentation (A) is correct because it artificially increases the diversity of the training set by adding noise or transformations, which helps the model generalize better and reduces overfitting. Feature selection using correlation analysis (E) is correct because it removes redundant or highly correlated features, simplifying the model and minimizing the risk of learning noise from irrelevant predictors.

Answer

SMOTE to balance classes

Answer

One-hot encoding of all categorical features

Answer

Log transformation of skewed features

Question 19

A team is preparing text data for a natural language processing (NLP) model. They have a corpus of customer reviews. Which THREE preprocessing steps are essential to reduce noise and improve model performance?

Accepted Answer

Remove punctuation and special characters. Option B is correct because punctuation and special characters (e.g., commas, exclamation marks) introduce irrelevant noise that does not carry semantic meaning for most NLP models. Removing them reduces vocabulary size and prevents the model from treating 'hello!' and 'hello' as distinct tokens, which improves generalization and reduces overfitting.

Answer

Apply one-hot encoding to each word

Answer

Compute TF-IDF vectors

Question 20

A retail company is preparing a dataset for a machine learning model to predict customer churn. The dataset includes customer_id, signup_date, last_purchase_date, total_purchases, average_order_value, and churn_label. The data scientist notices that the 'total_purchases' column has missing values for 15% of the records. The company wants to use AWS Glue for data preparation. Which approach should the data scientist take to handle the missing values while minimizing bias and preserving data integrity?

Accepted Answer

Use AWS Glue DynamicFrame to perform model-based imputation, predicting missing total_purchases using other features like average_order_value and signup_date.. Option C is correct because model-based imputation uses relationships between features (e.g., average_order_value and signup_date) to predict missing total_purchases values, minimizing bias compared to simple mean/median imputation. AWS Glue DynamicFrames support custom transformation logic, allowing you to implement a predictive model (e.g., using Spark MLlib) directly within the Glue ETL job. This approach preserves data integrity by leveraging existing data patterns rather than discarding records or introducing arbitrary constants.

Answer

Use AWS Glue DataBrew to fill missing values with the median of total_purchases.

Answer

Drop all records with missing total_purchases values.

Answer

Replace missing total_purchases with the mean of the non-missing values.