MLA-C01 Data Preparation for Machine Learning Practice Test 5 — 15 Questions

Question 1

A data scientist is using SageMaker built-in linear learner algorithm for a regression problem. The dataset has 10 features, some have missing values, and the target variable is right-skewed. The data scientist wants to handle missing values and transform the target variable to improve model performance. Which data preparation steps should the data scientist take?

Accepted Answer

Impute missing values with the median of each feature and apply a log transformation to the target variable.. Option C is correct because imputing missing values with the median is robust to outliers and preserves the distribution of each feature, which is important when the target is right-skewed. Applying a log transformation to the right-skewed target variable helps normalize its distribution, which aligns with the linear learner algorithm's assumption of normally distributed errors and improves convergence and prediction accuracy.

Answer

Apply one-hot encoding to all features and remove missing values by dropping rows.

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Standardize all features to have zero mean and unit variance, then apply a box-cox transformation to the target.

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Remove rows with missing values and normalize the target to range [0,1].

Question 2

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

Accepted Answer

Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.. Option B is correct because AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.

Answer

Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.

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Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.

Answer

Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.

Question 3

A data team is preparing data for a machine learning pipeline. Which TWO practices are best for ensuring data quality and reproducibility? (Choose two.)

Accepted Answer

Implement automated data validation checks to catch anomalies in new data.. Option C is correct because automated data validation checks (e.g., using AWS Glue DataBrew or Deequ on Amazon EMR) proactively catch schema drift, missing values, and distribution anomalies in new data, ensuring that only high-quality data enters the ML pipeline. This practice is essential for maintaining data quality at scale without manual intervention.

Answer

Use a fixed random seed when sampling data to ensure repeatability.

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Shuffle the dataset before splitting into train and test sets.

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Manually inspect and clean data to remove outliers.

Question 4

A data scientist is working with a dataset containing customer demographics and purchase history. The dataset includes categorical variables with high cardinality (e.g., ZIP code, product ID). The data scientist wants to perform feature engineering to improve model performance. Which THREE feature engineering techniques should the data scientist consider? (Choose three.)

Accepted Answer

Principal Component Analysis (PCA) to reduce dimensionality of numerical features.. Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated numerical features into a smaller set of uncorrelated principal components, capturing the maximum variance in the data. This is correct because the dataset includes numerical features (e.g., purchase amounts, age) where PCA can reduce noise and multicollinearity, improving model performance without losing critical information.

Answer

Domain-specific feature engineering based on business rules.

Answer

One-hot encoding all categorical features.

Question 5

A data engineer needs to provide the data science team with access to various data sources for machine learning. The team uses Amazon SageMaker Studio. Which TWO data sources can be accessed directly from SageMaker Studio notebooks without additional infrastructure? (Choose two.)

Accepted Answer

Amazon S3.. Amazon SageMaker Studio notebooks have a built-in SageMaker SDK that can directly read from and write to Amazon S3 using the `s3fs` filesystem or the SageMaker `s3_utils` module. This integration requires no additional infrastructure because S3 is the default storage backend for SageMaker, and the notebook environment is pre-configured with the necessary IAM roles and boto3 libraries to access S3 buckets directly.

Answer

Amazon Redshift.

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Amazon DynamoDB.

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Amazon RDS (MySQL).

Question 6

A data scientist needs to convert categorical variables to numerical format for a linear regression model. The dataset contains a 'Country' column with 50 unique values. Which transformation should the engineer use to avoid introducing ordinal relationships?

Accepted Answer

One-hot encoding. One-hot encoding is correct because it creates binary columns for each category, avoiding any implicit ordinal relationship between the 50 unique countries. This is essential for linear regression, which assumes numerical inputs have meaningful order; one-hot encoding ensures the model treats each country as an independent category without ranking.

Answer

Label encoding

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Target encoding

Answer

Ordinal encoding

Question 7

A company is building a fraud detection model on an imbalanced dataset (99% legitimate, 1% fraudulent). To improve recall on the minority class, they want to resample data. Which combination of techniques should they use?

Accepted Answer

SMOTE on training set only. SMOTE should be applied only to the training set to avoid data leakage; evaluation must reflect the original distribution. Random undersampling may discard useful majority samples; random oversampling before split leaks information.

Answer

SMOTE on entire dataset before train/test split

Answer

Random oversampling of minority class before train/test split

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Random undersampling of majority class

Question 8

A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multiple time zones. The engineer needs to create a feature for hour-of-day consistent across all records. Which approach ensures correctness?

Accepted Answer

Convert all timestamps to UTC during the ETL process, then extract hour. Option D is correct because converting all timestamps to UTC during the ETL process ensures a consistent time zone reference before extracting the hour-of-day feature. This avoids ambiguity from mixed time zones and aligns with best practices for machine learning feature engineering. AWS Glue ETL with Apache Spark provides built-in functions like `to_utc_timestamp()` to perform this conversion reliably.

Answer

Convert all timestamps to UTC in the ETL script using Spark's from_utc_timestamp

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Use AWS Glue's built-in transform to parse timestamps with timezone offsets

Answer

Use Python's datetime.strptime with tzlocal

Question 9

A machine learning engineer needs to handle missing values in a dataset containing numerical features. The missingness is completely at random (MCAR). Which imputation strategy is most robust for downstream model performance?

Accepted Answer

Impute with median of each feature. When missingness is completely at random (MCAR), imputing with the median is robust because it preserves the central tendency of the distribution without introducing bias or distorting variance. Unlike mean imputation, the median is resistant to outliers, making it a safe default for numerical features in downstream models that assume normally distributed inputs or are sensitive to skewed data.

Answer

Impute with a constant like -1

Answer

Use a model to predict missing values

Answer

Remove all rows with missing values

Question 10

A team is using Amazon SageMaker for feature engineering. They have a dataset with a column 'TransactionDate' in string format (e.g., '2023-01-15 10:30:00'). They need to create features: year, month, day, hour, and day_of_week. What is the most efficient way to do this in a SageMaker processing job?

Accepted Answer

Use pandas datetime functions and then split. Option A is correct because using pandas datetime functions within a SageMaker processing job is the most efficient approach for this task. SageMaker processing jobs run custom Python scripts, and pandas provides vectorized operations (e.g., `pd.to_datetime()`, `.dt.year`, `.dt.month`, `.dt.day`, `.dt.hour`, `.dt.dayofweek`) that parse the string column and extract all required features in a single pass without external dependencies or data movement.

Answer

Use SageMaker built-in first party algorithms

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Use AWS Glue for transformation

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Use SQL query in Athena on S3 data

Question 11

A data scientist is preparing text data for natural language processing (NLP). The corpus contains many rare words and typos. To reduce dimensionality and improve generalization, they decide to apply stemming and remove stop words. However, after training, the model performs poorly on domain-specific terms. What is the most likely cause?

Accepted Answer

Both stemming and stop word removal are inappropriate for the domain. Option B is correct because both stemming and stop word removal are inappropriate for this domain. Stemming aggressively reduces words to their root forms, which can conflate distinct domain-specific terms (e.g., 'therapy' and 'therapist' both stem to 'therap'), losing critical semantic nuance. Stop word removal can discard words that carry domain-specific meaning (e.g., 'not' in medical negation or 'up' in 'tune-up' for maintenance), leading to poor generalization on specialized vocabulary.

Answer

The corpus should be lemmatized instead

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Stemming is too aggressive for the domain

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Stop word removal removed important context words

Question 12

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

Accepted Answer

Temporal split based on date. Option B is correct because a temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.

Answer

Stratified split based on target

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Random split with 70/20/10

Answer

K-fold cross-validation

Question 13

A company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?

Accepted Answer

Resample data to a fixed frequency. Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.

Answer

Remove missing timestamps

Answer

Sort data by device ID

Answer

Impute missing values with forward fill

Question 14

A data scientist is using Amazon SageMaker Data Wrangler for feature engineering on a large dataset stored in S3. The dataset has a column 'ProductCategory' with 1000+ unique values. To reduce dimensionality, they want to group categories that appear less than 1% of the time into an 'Other' category. Which Data Wrangler transform should they use?

Accepted Answer

Handle rare values. The 'Handle rare values' transform in SageMaker Data Wrangler is specifically designed to group infrequent category values into a single 'Other' bucket based on a frequency threshold (e.g., less than 1%). This directly addresses the need to reduce dimensionality by consolidating rare categories without requiring custom code or manual grouping.

Answer

Group similar categories

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Custom transform with Python

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One-hot encode with threshold

Question 15

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 rows and 200 features, with 5% positive class. The engineer suspects class imbalance may affect model performance. Which TWO actions should the engineer take to mitigate imbalance? (Choose 2.)

Accepted Answer

Apply SMOTE only to training data. Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the class distribution. Applying SMOTE only to the training data is critical to avoid data leakage, as the test set must remain untouched to provide an unbiased evaluation of model performance on the original class distribution.

Answer

Perform PCA to reduce dimensions

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Remove features with low variance

Answer

Use k-fold cross-validation