MLA-C01 Data Preparation for Machine Learning Practice Test 8 — 15 Questions

Question 1

A data engineer is using Amazon Athena to query a partitioned dataset stored in S3. Which THREE actions are necessary to ensure the queries can access the data and run efficiently?

Accepted Answer

Store the underlying data in a columnar format like Parquet. Storing data in a columnar format like Parquet reduces the amount of data scanned by Athena because it reads only the columns required by the query, not entire rows. This directly lowers query cost and improves performance, especially on large datasets, as Parquet also supports compression and predicate pushdown.

Answer

Create an AWS Glue DataBrew recipe to transform the data

Answer

Add each partition manually using ALTER TABLE ADD PARTITION

Question 2

A company operates an IoT platform that ingests sensor data from thousands of devices. Data is streamed via Amazon Kinesis Data Streams and stored in an S3 bucket using a Kinesis Firehose delivery stream, which writes data in 5-minute windows. The data is then used to train a machine learning model for anomaly detection. Recently, the data science team noticed that the training dataset is always missing the last 5 minutes of events from the end of each day. The S3 objects show that the last delivery stream buffer window is incomplete. The data engineer checked the Kinesis Firehose metrics and found no delivery errors or data loss, but the 'IncomingBytes' and 'IncomingRecords' metrics show consistent data for all periods. The S3 bucket has Lifecycle policies that do not delete objects. The team suspects the issue is related to the data preparation pipeline. Which course of action would correctly resolve the missing data problem?

Accepted Answer

Increase the buffer size to 10 MB and reduce the buffer interval to 60 seconds in the Firehose delivery stream configuration. Option A is correct because the issue is that the last 5-minute buffer window at the end of each day never completes, so Firehose never delivers that final object to S3. By reducing the buffer interval to 60 seconds and increasing the buffer size to 10 MB, Firehose will flush data more frequently, ensuring that even small residual data at the end of the day is delivered before the stream stops. This directly addresses the incomplete last window without requiring reprocessing or changing the pipeline architecture.

Answer

Reprocess the Kinesis stream data from the beginning using a custom application

Answer

Modify the data preparation pipeline to use AWS Lambda to write data to S3 directly from Kinesis

Answer

Increase the buffer interval to 600 seconds to allow more time for data to accumulate

Question 3

A data scientist is preparing a dataset for a binary classification model to predict customer churn. The dataset contains a timestamp column 'signup_date' that is not relevant for the prediction. What is the most appropriate action to handle this column?

Accepted Answer

Drop the 'signup_date' column from the dataset.. Option D is correct because the 'signup_date' column is explicitly stated as not relevant for the prediction. In binary classification for customer churn, including an irrelevant timestamp can introduce noise, increase dimensionality, and potentially cause overfitting. Dropping the column is the most appropriate action to maintain model simplicity and focus on predictive features.

Answer

Apply one-hot encoding to the year, month, and day components.

Answer

Convert the timestamp to a numeric feature (e.g., days since signup) and include it.

Answer

Use leave-one-out encoding based on the target variable.

Question 4

A machine learning engineer is building a regression model to predict house prices. The feature 'square_footage' has values ranging from 500 to 10,000, while 'num_bedrooms' ranges from 1 to 10. Which preprocessing step is most critical before training a model that uses gradient descent?

Accepted Answer

Standardize both features to have zero mean and unit variance.. Gradient descent is sensitive to the scale of features because it updates weights proportionally to the feature values. With 'square_footage' (500–10,000) and 'num_bedrooms' (1–10), the large range difference causes the loss function's contours to be elongated, leading to slow or unstable convergence. Standardizing both features to zero mean and unit variance ensures each feature contributes equally to the gradient updates, enabling faster and more reliable optimization.

Answer

Apply a logarithmic transformation to both features.

Answer

Encode the 'num_bedrooms' feature using one-hot encoding.

Answer

Impute missing values using the mean of the feature.

Question 5

A company uses Amazon SageMaker Data Wrangler to create a data flow for a classification model. The dataset contains a high-cardinality categorical feature 'product_id' with 50,000 unique values. The data scientist wants to reduce dimensionality while preserving predictive power. Which approach is most effective?

Accepted Answer

Perform target encoding by replacing each product ID with the average target value for that product.. Target encoding is the most effective approach for high-cardinality categorical features because it replaces each category with the mean of the target variable, preserving predictive signal while drastically reducing dimensionality. In SageMaker Data Wrangler, this can be implemented using the 'Encode categorical' transform with the 'Target encoding' option, which avoids the explosion of features caused by one-hot encoding and retains the relationship between product IDs and the target.

Answer

Apply one-hot encoding to the 'product_id' column.

Answer

Use feature hashing to map product IDs to a fixed number of buckets (e.g., 100).

Answer

Drop the 'product_id' column entirely.

Question 6

A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?

Accepted Answer

Use AWS Glue to create a crawler and ETL job that converts CSV to Parquet.. AWS Glue is the correct choice because it provides a serverless, pay-per-use ETL service that can automatically convert CSV to Parquet without writing custom code. The Glue crawler infers the schema, and the ETL job uses built-in transforms to efficiently handle 10 TB of data with minimal cost, as it only charges for the resources consumed during the job execution.

Answer

Use SageMaker Processing with a TensorFlow script to read CSV and write Parquet.

Answer

Use Amazon S3 Select to convert the data to Parquet during retrieval.

Answer

Use Amazon EMR with a Spark job to convert the files.

Question 7

A data science team is building a model to predict fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. They plan to use Amazon SageMaker to train a model. Which data preparation technique should they apply to address the severe class imbalance before training?

Accepted Answer

Use SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraudulent samples.. Option D is correct because using SMOTE generates synthetic samples for the minority class, addressing imbalance without simply duplicating data. Option A is wrong because oversampling with duplication can lead to overfitting. Option B is wrong because undersampling discards too much legitimate data, losing valuable patterns. Option C is wrong because the data is already in a tabular format, not images.

Answer

Apply data augmentation using image transformations because fraud detection is like image classification.

Answer

Randomly oversample the fraudulent class to match the legitimate count by duplicating existing fraud records.

Answer

Randomly undersample the legitimate class to 1,000 samples to create a balanced dataset.

Question 8

A company is training a deep learning model on Amazon SageMaker using a dataset stored in Amazon S3. The training job is taking a long time due to I/O bottlenecks. The data is in JSON lines format. Which data preparation step combined with SageMaker's best practices would most effectively reduce training time?

Accepted Answer

Convert the data to RecordIO-Protobuf format and use SageMaker's Pipe mode for training.. Option C is correct because converting JSON lines data to RecordIO-Protobuf format allows SageMaker's Pipe mode to stream data directly from Amazon S3 to the training algorithm without writing to disk, eliminating I/O bottlenecks. Pipe mode uses a FIFO pipe (named pipe) to feed data sequentially, which significantly reduces training time for deep learning models that iterate over the dataset multiple times.

Answer

Convert the JSON lines files to CSV format and use SageMaker's File mode for training.

Answer

Compress the JSON lines files using gzip and use File mode with local caching.

Answer

Split the data into multiple smaller files and use multiple training instances to parallelize.

Question 9

A data engineer is using Amazon SageMaker Processing to run a data preprocessing script on a dataset with 500 million rows. The script runs out of memory on a single ml.r5.24xlarge instance. The engineer needs to modify the processing job to handle the dataset size. Which approach is most cost-effective and scalable?

Accepted Answer

Configure the Processing job with multiple instances and use ShardedByS3Key for data splitting.. Option A is correct because SageMaker Processing with ShardedByS3Key splits the input dataset by S3 object boundaries across multiple instances, allowing distributed processing of the 500 million rows without exceeding memory on any single instance. This approach is cost-effective as it uses multiple smaller instances (e.g., ml.r5.xlarge) rather than a single oversized instance, and scales linearly with data size.

Answer

Write the script to process data in chunks and write intermediate results to local ephemeral storage.

Answer

Increase the instance type to a larger one like ml.p3dn.24xlarge with more memory.

Answer

Reduce the number of instances to one and increase the volume size for swap space.

Question 10

Which TWO actions are recommended best practices when preparing training data for a machine learning model in AWS? (Choose two.)

Accepted Answer

Check for and handle missing values appropriately.. Option C is correct because missing values can introduce bias or cause algorithms to fail, so handling them (e.g., via imputation or removal) is a critical data preparation step in AWS SageMaker. Option D is correct because splitting data into training, validation, and test sets allows you to evaluate model performance on unseen data and prevent overfitting, which is a standard practice in SageMaker's built-in algorithms and training jobs.

Answer

Remove all outliers from the dataset.

Answer

Train the model on the entire dataset to maximize data usage.

Answer

Always normalize all features to a [0,1] range.

Question 11

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. Which TWO features of Data Wrangler can be used to handle imbalanced classification problems? (Choose two.)

Accepted Answer

The Random Oversampling transform to duplicate minority class instances.. Option A is correct because Amazon SageMaker Data Wrangler includes a built-in Random Oversampling transform that duplicates instances of the minority class to balance the class distribution. This directly addresses imbalanced classification by increasing the representation of the underrepresented class without generating synthetic data.

Answer

The Drop Duplicates transform to remove redundant rows.

Answer

Standardization to scale numerical features.

Answer

One-hot encoding for categorical variables.

Question 12

A company is preparing a large dataset for a SageMaker built-in XGBoost model. The dataset has missing values in both numeric and categorical features, and some categorical features have high cardinality. Which THREE data preparation steps should the company take to optimize model performance? (Choose three.)

Accepted Answer

Split the data into training, validation, and test sets before any imputation.. Option B is correct because splitting the data into training, validation, and test sets before any imputation prevents data leakage. If imputation statistics (e.g., mean, median) were computed on the full dataset, information from the validation and test sets would influence the training data, leading to overly optimistic performance estimates and poor generalization to new data.

Answer

Remove any rows with outlier values.

Answer

Apply target encoding to all categorical features regardless of cardinality.

Question 13

A company runs an online retail business and wants to build a product recommendation system. They have a dataset of customer purchases stored in Amazon S3 as CSV files. The dataset includes columns: 'customer_id', 'product_id', 'purchase_date', 'quantity', 'price', and 'category'. The data science team plans to use Amazon SageMaker to train a factorization machines model. During data exploration, they discover that the 'category' column has 1,200 unique values, and many categories appear only a few times. The 'product_id' column has 50,000 unique values. They want to include both features in the model. The team is concerned about the high cardinality of these features. Which approach should they take to prepare these features for the factorization machines model?

Accepted Answer

Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.. Option C is correct because Amazon SageMaker's factorization machines algorithm natively supports categorical features encoded as integer indices (0-based). This avoids the explosion of features from one-hot encoding (which would create 51,200 columns) and leverages the algorithm's ability to learn interactions between high-cardinality features via factorized parameters, making it both memory-efficient and effective for sparse data.

Answer

Apply one-hot encoding to both 'product_id' and 'category' columns.

Answer

Drop the 'category' column and only use 'product_id' since it has more granularity.

Answer

Apply principal component analysis (PCA) to reduce the dimensionality of the categorical features.

Question 14

A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?

Accepted Answer

Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.. Option B is correct because XGBoost handles missing values natively, so median imputation for 'blood_pressure' is robust to outliers and preserves data distribution, while integer encoding (label encoding) for 'diagnosis_code' with 10,000 unique values is efficient and avoids the dimensionality explosion of one-hot encoding. AWS Glue ETL can apply these transformations using built-in functions like `Imputer` and `StringIndexer` without excessive memory overhead.

Answer

Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.

Answer

Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.

Answer

Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.

Question 15

A financial services company is developing a fraud detection model using Amazon SageMaker. They have a dataset with 10 million transactions, each with 300 features. The dataset is highly imbalanced (0.1% fraud). They have performed feature engineering and now need to split the data for training, validation, and test sets. The data is stored in CSV files in Amazon S3. They plan to use SageMaker's built-in XGBoost algorithm. To ensure proper evaluation and avoid data leakage, which data splitting strategy should they use?

Accepted Answer

Perform a stratified split on the target variable to ensure each set has the same fraud ratio.. Option C is correct because a stratified split preserves the original 0.1% fraud ratio across training, validation, and test sets, which is critical for imbalanced datasets. This ensures each subset is representative of the population, allowing SageMaker's XGBoost to be evaluated fairly without data leakage. Random splits (Option A) could accidentally create a validation or test set with zero fraud cases, making evaluation meaningless.

Answer

Randomly shuffle the entire dataset and then split into 80% training, 10% validation, 10% test.

Answer

Use k-fold cross-validation on the entire dataset and average the results.

Answer

Apply SMOTE to balance the dataset first, then split randomly into training, validation, and test sets.