MLA-C01 Data Preparation for Machine Learning Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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.

Question 6

A financial services company is building a fraud detection model using transactional data stored in Amazon S3. The data includes transaction_id, timestamp, amount, merchant_category, and fraud_label (0/1). The data is collected from multiple sources and has inconsistencies: timestamps are in different timezones (UTC and EST), merchant categories are sometimes misspelled (e.g., 'RESTAURANT', 'Restaurant', 'restaurant'), and the fraud_label is missing for about 5% of records. The data science team uses AWS Glue for ETL. They need to prepare a clean dataset for training. The final dataset must have consistent timestamps in UTC, standardized merchant categories, and no missing fraud labels. The team also wants to minimize data loss. Which set of actions should the team take?

Accepted Answer

Use AWS Glue to convert timestamps to UTC, use a mapping table to group similar merchant categories (e.g., all restaurant variants to 'Restaurant'), and impute missing fraud_label using mode (most frequent value).. Option D is correct because it preserves data by imputing missing fraud labels using the mode (most frequent value), which is appropriate for a binary classification label where the majority class is likely 0. It also standardizes timestamps to UTC and uses a mapping table to group merchant category variants, ensuring consistency without data loss. Dropping records (as in A and C) would reduce the dataset size, and imputing with the mean (as in B) is invalid for a categorical label.

Answer

Use AWS Glue to convert all timestamps to UTC, apply a mapping function to correct merchant category misspellings to a standard list, and drop records with missing fraud_label.

Answer

Use AWS Glue to convert timestamps to UTC, use a fuzzy matching algorithm to standardize merchant categories, and replace missing fraud_label with the mean value (0.05).

Answer

Use AWS Glue to convert timestamps to UTC, correct merchant categories by mapping known misspellings to correct names, and drop records with missing fraud_label.

Question 7

A healthcare startup is building a model to predict patient readmission within 30 days. The data is stored in Amazon Redshift and includes patient demographics, admission history, lab results, and medication records. The data scientist extracts a sample of 10,000 records to Amazon S3 as CSV files for initial prototyping. During exploratory data analysis, they find that the 'age' column has values like '150', '0', and negative numbers. The 'diagnosis_code' column contains codes like 'E11', 'E11.9', and 'e11' (inconsistent formatting). The 'readmitted' target column has 60% 'Yes' and 40% 'No'. The data scientist wants to use AWS Glue DataBrew for data cleaning. Which combination of steps should they use?

Accepted Answer

In AWS Glue DataBrew: 1) Filter age between 0 and 120 to remove invalid values. 2) Standardize diagnosis_code to uppercase using a formula. 3) Apply Random Oversampling to balance the target column.. Option A is correct because it uses AWS Glue DataBrew's built-in capabilities to filter invalid age values (0–120), standardize the diagnosis_code to uppercase via a formula, and apply Random Oversampling to address the 60/40 class imbalance. DataBrew supports filtering, formula-based transformations, and built-in ML transforms like Random Oversampling, making this combination valid and efficient for data cleaning.

Answer

In AWS Glue DataBrew: 1) Impute age with the mean. 2) Apply Standard Scaler to all numeric columns. 3) Use Random Oversampling to balance the target column.

Answer

In AWS Glue DataBrew: 1) Replace age with median. 2) Convert diagnosis_code to uppercase. 3) Apply SMOTE to balance the target column.

Answer

In AWS Glue DataBrew: 1) Remove rows where age is outside 0-120. 2) Drop diagnosis_code column. 3) Use Random Undersampling to balance the target column.

Question 8

An e-commerce company is building a recommendation system using user interaction data stored in Amazon DynamoDB. The data includes user_id, product_id, timestamp, event_type (click, add_to_cart, purchase), and session_id. The data science team exports the data to Amazon S3 as JSON files. During preprocessing, they discover that the 'event_type' field contains inconsistent values due to logging errors: 'Click', 'click', 'CLICK', and 'clck' all appear. Also, there are duplicate records where the same user_id, product_id, and timestamp appear multiple times with the same event_type. The team wants to use AWS Glue to clean the data for training a sequence-based recommendation model. Which set of actions should they perform?

Accepted Answer

Use AWS Glue to drop exact duplicate rows (all columns identical). Then apply a mapping function to standardize event_type to a controlled vocabulary (e.g., 'click', 'add_to_cart', 'purchase').. Option B is correct because it addresses both data quality issues: first, dropping exact duplicate rows (all columns identical) removes redundant records that would bias the sequence model; second, standardizing event_type to a controlled vocabulary ensures consistent categorical input for ML training. AWS Glue's DynamicFrame with DropDuplicates and Map transformations are the appropriate tools for this ETL task.

Answer

Use AWS Glue to group records by session_id and aggregate event_types into a list per session. Then apply a mapping function to standardize event_type names.

Answer

Use AWS Glue to drop duplicate records based on all columns. Then drop the event_type column and use only numeric features for training.

Answer

Use AWS Glue to impute event_type with the mode for records with inconsistent values. Then drop duplicate records based on user_id, product_id, and timestamp.

Question 9

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

Accepted Answer

Random oversampling of the minority class. Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.

Answer

Principal Component Analysis (PCA) dimensionality reduction

Answer

Standard scaling of numerical features

Answer

One-hot encoding of categorical variables

Question 10

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

Accepted Answer

Word embeddings (e.g., Word2Vec or GloVe). Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

Answer

One-hot encoding of each word

Answer

Bag-of-words with TF-IDF

Answer

Hashing vectorizer

Question 11

A company uses Amazon SageMaker Data Wrangler to prepare data for ML. The dataset contains a timestamp column and sensor readings from IoT devices. The data scientist needs to create features such as moving averages and rolling statistics over time windows. Which Data Wrangler transformation type should be selected?

Accepted Answer

Window function. Window functions in Amazon SageMaker Data Wrangler allow you to compute moving averages, rolling statistics, and other time-window-based aggregations over ordered partitions of data. This is the correct transformation type because it directly supports operations like `SUM() OVER (ORDER BY timestamp ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)` without requiring custom code or losing row-level granularity.

Answer

Join

Answer

Custom Python script

Answer

Group by and aggregate

Question 12

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

Accepted Answer

Parquet. Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.

Answer

XML

Answer

ORC

Answer

JSON Lines

Question 13

A data scientist is using Amazon SageMaker Processing to run a feature engineering job. The job requires installing additional Python libraries not included in the default SageMaker containers. Which approach should the data scientist use to include these libraries?

Accepted Answer

Create a custom Docker image with the libraries installed and specify it in the ProcessingInput. Option B is correct because SageMaker Processing jobs run in isolated containers that cannot install packages at runtime via pip without internet access or custom images. Creating a custom Docker image with the required libraries pre-installed ensures the environment is consistent, reproducible, and avoids dependency resolution failures during job execution. This approach aligns with SageMaker's best practice for custom dependencies.

Answer

Add the libraries to the `requirements.txt` file in the same S3 bucket as the script

Answer

Use Amazon EFS to store the libraries and mount them to the processing container

Answer

Use the `pip install` command within the processing script at runtime

Question 14

A machine learning team is building a model to predict customer churn. They have historical data that includes customer activity logs, each with a timestamp. The team wants to ensure that the training data does not contain any data leakage from the future. Which approach should they take when preparing the training and validation datasets?

Accepted Answer

Split the data by time, using data before a certain date for training and after for validation. Option D is correct because splitting by time (chronological split) prevents data leakage by ensuring that the validation set contains only future data relative to the training set. In time-series or timestamped data, random splits can allow the model to learn from future patterns, artificially inflating performance. This approach respects the temporal dependency inherent in customer churn prediction.

Answer

Use stratified sampling based on churn label

Answer

Randomly split the data 80/20 for training and validation

Answer

Use k-fold cross-validation with shuffling

Question 15

A data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?

Accepted Answer

Handle missing values (with median strategy). Option D is correct because Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing custom code.

Answer

Replace missing with constant

Answer

Custom transform with Python

Answer

Drop missing rows