MLA-C01 Data Preparation for Machine Learning — 50 Questions

Question 1

A SageMaker Processing job fails with the error: 'Unable to parse CSV file due to inconsistent number of columns'. The data is stored as CSV in S3. What is the most likely cause?

Accepted Answer

Some rows have missing values causing fewer columns. The 'inconsistent number of columns' error in a SageMaker Processing job directly indicates that some rows in the CSV file have fewer fields than expected. SageMaker's built-in CSV parser expects a uniform number of columns per row; missing values (e.g., trailing commas omitted or blank fields not represented) cause row lengths to differ, triggering this specific parsing failure.

Answer

The CSV file is missing a header row

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The file uses a different delimiter like tab

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Some fields contain quoted commas

Question 2

A company has 10 TB of log data in compressed JSON format stored in Amazon S3. The data needs to be processed and transformed into a structured format for machine learning. The processing requires complex transformations, including parsing nested JSON and joining with a reference table. The company wants to minimize infrastructure management. Which approach should the company use?

Accepted Answer

Use AWS Glue ETL with PySpark.. AWS Glue ETL with PySpark (Option D) is the best choice because it provides a fully serverless environment, minimizing infrastructure management. Glue can handle complex transformations like parsing nested JSON and joining with reference tables using PySpark, and it scales automatically for large datasets (10 TB). Amazon EMR (Option C) requires cluster management and provisioning, which contradicts the goal of minimizing management overhead.

Answer

Use SageMaker Processing jobs to run custom scripts.

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Use Amazon Athena to query and transform the data.

Answer

Use Amazon EMR with Apache Spark.

Question 3

A company needs to anonymize personally identifiable information (PII) in a dataset before using it for ML. The dataset is stored in S3 as CSV files. The team wants to mask credit card numbers by replacing all digits except the last four with asterisks. Which approach is the most scalable?

Accepted Answer

Use AWS Glue with a custom PySpark UDF to apply regex masking. The most scalable because AWS Glue with PySpark UDFs can process large datasets (e.g., 500 GB) in a distributed manner across multiple nodes, handling the masking transformation efficiently. Option A (Amazon Comprehend) adds unnecessary cost and latency for simple regex-based masking, and Option B (Python shell job) runs on a single node and does not scale to handle 500 GB effectively.

Answer

Use Amazon Comprehend to detect PII and then a custom script to mask

Answer

Use a custom script in AWS Glue Python shell job with regex

Question 4

Refer to the exhibit. A data engineer runs a Glue ETL job that uses a Python script. The job fails because of a missing module `scikit-learn`. Which fix is MOST appropriate?

Accepted Answer

Add a --additional-python-modules argument to the job with scikit-learn. AWS Glue jobs (both ETL and Python shell) support the --additional-python-modules job parameter, which allows you to specify extra Python packages to install. This is the standard way to add libraries like scikit-learn that are not pre-installed. It avoids the need to install at runtime (Option A) or switch job types unnecessarily (Option D). Option C switching to Spark would add complexity and not directly solve the missing module.

Answer

Modify the script to install scikit-learn using pip at runtime

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Switch to a Glue job using Spark instead of Python

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Use a Glue Python shell job instead

Question 5

A data scientist needs to prepare a dataset for a binary classification model. The dataset contains 100,000 records with 50 features, including categorical variables with high cardinality, missing values in 30% of records for a key numeric feature, and a severe class imbalance (5% positive class). The data is stored in an Amazon S3 bucket. Which TWO actions should the data scientist take to improve model performance and ensure robust data preparation? (Choose two.)

Accepted Answer

Use stratified sampling to split the dataset into training and test sets, preserving the class imbalance ratio.. Stratified sampling preserves the class imbalance ratio (5% positive class) in both training and test sets, ensuring the model is evaluated on a representative distribution and avoiding overfitting to the majority class in training. Option E is correct because standardizing numeric features with scikit-learn's StandardScaler in an AWS Glue job ensures that features have zero mean and unit variance, which is critical for distance-based algorithms (e.g., SVM, k-NN) and many neural networks. Option B is incorrect because deleting records with missing values would discard 30% of data, leading to significant information loss and potential bias, and is especially harmful when missingness is non-random. Option C is incorrect because one-hot encoding high-cardinality categorical features creates a very large number of dummy variables, causing the curse of dimensionality and making the model computationally expensive and prone to overfitting. Option D is incorrect because random undersampling of the majority class discards potentially valuable data, often reducing model performance; techniques like SMOTE or class weights are preferred to address class imbalance.

Answer

Delete all records with missing values to ensure data integrity.

Answer

Apply one-hot encoding to all categorical features regardless of cardinality.

Answer

Randomly undersample the majority class to balance the dataset before training.

Question 6

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 7

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.. 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 8

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.. 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.

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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 9

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.. '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 10

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 11

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 12

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.

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Scale values transform.

Answer

Handle missing transform.

Question 13

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.. 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 14

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.. 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 15

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.. 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 16

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 17

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

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One-hot encoding

Answer

Frequency encoding

Question 18

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 19

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. 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 20

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