MLA-C01 Data Preparation for Machine Learning Practice Test 11 — 15 Questions

Question 1

A data scientist is preparing a dataset stored in Amazon S3 for a SageMaker training job. The dataset contains missing values in several columns. The scientist wants to impute missing values with the mean of each column. Which SageMaker built-in algorithm or processing method should be used to perform this imputation efficiently?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use the SageMaker 'Impute' transform in Data Wrangler.. Mean imputation is a common preprocessing step for numerical features. In SageMaker Data Wrangler, the 'Impute' transform provides a user-friendly way to replace missing values with the column mean, median, or mode. This transform can be applied to multiple columns and is part of the data preparation flow. It ensures that the dataset is complete before feeding it into a training algorithm, which is essential for algorithms that cannot handle missing values natively.

Answer

Use the SageMaker 'PCA' algorithm to fill missing values.

Answer

Use the SageMaker 'Linear Learner' algorithm to predict missing values.

Answer

Use the SageMaker 'XGBoost' algorithm to predict missing values.

Question 2

A data engineer must prepare a 4 TB Parquet dataset stored in Amazon S3 for a SageMaker training job that runs on 8 ml.p4d.24xlarge instances. The engineer wants the fastest possible data throughput during training while minimizing per-epoch I/O overhead. The dataset is immutable for the duration of the training run. Which approach BEST meets these requirements?

Accepted Answer

Copy the dataset once into an Amazon FSx for Lustre file system linked to the S3 bucket, and point the training job's channel at the FSx mount.. FSx for Lustre linked to S3 offers a shared, high-throughput file system that materializes data on first access and serves subsequent epochs at much lower latency than repeated S3 reads. Because the dataset is immutable during training, the lazy-load-then-cache behavior is safe and efficient, and all eight instances read from the same fast mount rather than each pulling from S3.

Answer

Use SageMaker FastFile mode, which streams objects from S3 on demand with a local cache and exposes a POSIX-like interface.

Answer

Use SageMaker File mode, which copies the dataset to the attached Amazon EBS volume before training begins.

Answer

Use SageMaker Pipe mode with the dataset converted to protobuf RecordIO and streamed directly to the training algorithm.

Question 3

A machine learning engineer is using Amazon SageMaker Processing to preprocess a large dataset. The processing job runs a custom Python script that uses the pandas library to read multiple CSV files from an S3 input prefix. The script must write the processed output to a different S3 prefix. Which configuration of the ProcessingInput and ProcessingOutput parameters is correct for this scenario?

Accepted Answer

ProcessingInput with source set to the S3 input prefix and destination set to '/opt/ml/processing/input'; ProcessingOutput with source set to '/opt/ml/processing/output' and destination set to the S3 output prefix.. In SageMaker Processing, ProcessingInput specifies the S3 source and the local destination path where the data will be mounted in the container. ProcessingOutput specifies the local source path where the script writes output and the S3 destination where it will be uploaded. This bidirectional mapping ensures that the container can access input data and persist output data to S3. The correct configuration uses S3 URIs for external locations and local paths for container locations.

Answer

ProcessingInput with source set to the S3 input prefix and destination set to the S3 output prefix; ProcessingOutput with source set to the S3 input prefix and destination set to '/opt/ml/processing/output'.

Answer

ProcessingInput with source set to '/opt/ml/processing/input' and destination set to '/opt/ml/processing/output'; ProcessingOutput with source set to the S3 input prefix and destination set to the S3 output prefix.

Answer

ProcessingInput with source set to '/opt/ml/processing/input' and destination set to the S3 input prefix; ProcessingOutput with source set to the S3 output prefix and destination set to '/opt/ml/processing/output'.

Question 4

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset for a regression model. The dataset contains a categorical feature with high cardinality (over 10,000 unique values). The engineer wants to encode this feature efficiently without creating thousands of binary columns, which would explode the feature space. Which encoding technique should the engineer use in Data Wrangler?

Accepted Answer

Target encoding. Target encoding is designed for high-cardinality categorical features, replacing each category with a statistic of the target variable. This produces a single feature, avoiding the curse of dimensionality. In SageMaker Data Wrangler, target encoding is available as a transformation and can be configured with smoothing to reduce overfitting, making it the appropriate choice for this regression scenario.

Answer

Ordinal encoding

Answer

Binary encoding

Answer

One-hot encoding

Question 5

A data scientist is using SageMaker Data Wrangler to prepare a dataset for a binary classification model. The dataset contains a mix of numerical and categorical features. The scientist wants to perform feature engineering to improve model performance. Which TWO actions are appropriate for handling categorical features in Data Wrangler? (Choose two.)

Accepted Answer

Apply one-hot encoding to categorical features with low cardinality.. Categorical features need to be encoded into numerical representations before training. One-hot encoding is effective for low-cardinality features, creating binary indicators. Target encoding is suitable for high-cardinality features, replacing categories with target statistics and reducing dimensionality. Both are available as transforms in SageMaker Data Wrangler. Scaling, PCA, and log transformations are designed for numerical features and are not applicable to categorical data.

Answer

Apply PCA to categorical features for dimensionality reduction.

Answer

Apply log transformation to categorical features.

Answer

Apply min-max scaling to categorical features.

Question 6

A data scientist is preparing a CSV dataset in Amazon S3 for a SageMaker training job. Several rows contain missing values in numeric feature columns, and the chosen algorithm cannot handle NaNs. The scientist wants a repeatable, code-based transformation that runs inside a SageMaker Processing job before training. Which step is the MOST appropriate?

Accepted Answer

Write a preprocessing script that uses pandas to impute or drop missing values and run it in a SageMaker Processing job with a scikit-learn container.. Running a pandas-based cleaning script in a SageMaker Processing job makes the imputation or row removal reproducible and code-driven, and the job reads from and writes to S3 so the cleaned dataset is available to training. This is the canonical pattern for repeatable preprocessing that must run before a training job and be re-executed as data changes.

Answer

Use an S3 Lifecycle rule to expire objects containing missing values so only clean data remains in the bucket.

Answer

Open the CSV in a SageMaker notebook, manually edit the missing cells, and save the file back to S3.

Answer

Configure the SageMaker training job's input channel with a content type that tells the algorithm to ignore NaN values automatically.

Question 7

A data engineer is preparing a dataset in Amazon SageMaker Data Wrangler for a binary classification model. The dataset contains missing values in several numeric columns, and the engineer wants a reusable, reproducible transformation that can be applied identically to the training data and to future inference data. The engineer plans to export the transformation and integrate it into a SageMaker Pipeline. Which TWO actions should the engineer take to ensure the imputation is consistent between training and inference? (Choose two.)

Accepted Answer

Configure the imputation transform in the Data Wrangler flow so it learns the fill statistic (such as the mean) from the training data during the flow run.. Consistency between training and inference requires that the imputation statistic be learned from training data and that the same transformation be reused at inference. Configuring Data Wrangler to learn the fill statistic from training data captures the parameters, and exporting the flow into a SageMaker Pipeline processing step ensures those exact parameters and logic are applied to both training and inference inputs.

Answer

Replace missing numeric values with zero in both the training and inference datasets using a custom Python script.

Answer

Compute the mean of each numeric column on the full dataset including inference data, then hard-code those values into the pipeline.

Answer

Delete all rows that contain missing values from the training dataset before training.

Question 8

A machine learning engineer is preparing a dataset for training a model on Amazon SageMaker. The dataset contains numerical features with varying scales, and the engineer wants to ensure that all features contribute equally during training. Which data preparation step should the engineer take?

Accepted Answer

Apply feature scaling, such as standardization or normalization, to the numerical features.. Feature scaling adjusts the range or distribution of numerical features so that they are on a similar scale. This is crucial for algorithms that rely on distance calculations or gradient descent, as unscaled features can lead to slow convergence or biased results. Standardization or normalization are appropriate methods to achieve equal contribution from all features.

Answer

Apply one-hot encoding to all numerical features.

Answer

Remove outliers from the dataset.

Answer

Apply principal component analysis (PCA) to reduce dimensionality.

Question 9

A data engineer is preparing a dataset for a time series forecasting model. The dataset contains a timestamp column and a target variable. The engineer wants to create additional features such as lag values and rolling averages. Which SageMaker Data Wrangler transform should be used to generate these time series features?

Accepted Answer

Use the 'Time Series' transform to create lag and rolling window features.. Time series feature engineering often involves creating lag features (past values) and rolling statistics (e.g., moving averages) to capture temporal patterns. In SageMaker Data Wrangler, the 'Time Series' transform provides a dedicated set of operations for these tasks, including lag, rolling window, and date part extraction. This transform simplifies the process and ensures correct handling of time order. Other transforms like text featurization or outlier handling serve different purposes and cannot produce these features.

Answer

Use the 'Handle Outliers' transform to create lag features.

Answer

Use the 'Balance Data' transform to generate rolling averages.

Answer

Use the 'Featurize Text' transform to extract date parts.

Question 10

A data engineer is preparing a large training dataset stored in Amazon S3 as many small Parquet files, and a SageMaker training job that reads directly from S3 is spending most of its time on the input channel rather than on model computation. The engineer needs to improve the input throughput without changing the model code or the training algorithm. Which action should the engineer take?

Accepted Answer

Use the SageMaker File System Input with Amazon FSx for Lustre linked to the S3 bucket.. Feeding a training job from a high-performance shared file system removes the request-per-small-object overhead that dominates when many tiny Parquet files are read directly from S3. Amazon FSx for Lustre linked to the S3 bucket presents the data as files that the training container mounts and reads at high throughput, improving the input channel without altering model code or the training algorithm, which is exactly what the scenario requires.

Answer

Enable SageMaker Training Compiler and set the framework to accelerated mode.

Answer

Increase the number of records per S3 GET request by enabling S3 Transfer Acceleration on the bucket.

Answer

Convert the output to TFRecord format and use Pipe mode with the SageMaker TensorFlow estimator.

Question 11

A machine learning engineer is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The engineer needs to ensure that the feature group can serve both batch and real-time predictions. The feature group is configured with an online store enabled. Which additional configuration is required to support batch predictions?

Accepted Answer

Enable an offline store for the feature group and specify an S3 bucket for storage.. Feature Store's online store is for real-time serving, while the offline store is for batch serving and training. To support batch predictions, the feature group must have an offline store enabled, which stores feature data in S3. This allows the engineer to retrieve historical features in bulk for batch inference jobs. Without an offline store, only real-time serving is possible.

Answer

Increase the online store's read capacity to handle batch loads.

Answer

Set the feature group's event time to the ingestion time to allow batch queries.

Answer

Configure the feature group to use a custom KMS key for encryption, which enables batch access.

Question 12

A data engineer is using Amazon SageMaker Data Wrangler to prepare a dataset for a machine learning model. The engineer needs to ensure that the data preparation flow is reproducible and can be integrated into an MLOps pipeline. Which TWO actions should the engineer take to achieve this? (Choose two.)

Accepted Answer

Export the Data Wrangler flow to a SageMaker Pipeline as a processing step.. To ensure reproducibility and MLOps integration, the Data Wrangler flow should be exported either as a SageMaker Pipeline processing step or as a Python script committed to version control. Both methods capture the data preparation logic in a versionable, executable form that can be automated and tracked, meeting the requirements.

Answer

Manually document each transformation step in a text file and apply them using a Jupyter notebook.

Answer

Use the Data Wrangler interface to directly train a model without exporting the flow.

Answer

Use Data Wrangler's built-in scheduling feature to run the flow daily.

Question 13

A machine learning team stores training data in an Amazon S3 bucket and wants to catalog it so that Amazon Athena and Amazon SageMaker Feature Store can discover the schema. The data is partitioned by year, month, and day in Hive-style prefixes, and new partitions are added daily. A data engineer must ensure new partitions are automatically discoverable without manual intervention. Which solution meets these requirements?

Accepted Answer

Create an AWS Glue crawler with a daily schedule that points at the S3 prefix and updates the Data Catalog.. A scheduled AWS Glue crawler is the standard mechanism for automatically discovering new Hive-style partitions in S3 and refreshing the Data Catalog. Because it runs on a daily schedule, new year/month/day partitions appear in the catalog without any manual command, and Athena and other catalog-aware services can query them immediately.

Answer

Enable S3 Inventory and point Athena at the inventory report to enumerate partitions.

Answer

Run MSCK REPAIR TABLE on an Athena table each time new data arrives.

Answer

Register the S3 prefix as a SageMaker Feature Store offline store and let it infer the partitions.

Question 14

A machine learning engineer is preparing data for a SageMaker training job and needs to split a large dataset into training, validation, and test sets while avoiding data leakage from the same entity appearing in multiple splits. The dataset contains multiple rows per customer, and the target is customer churn. Which TWO strategies are appropriate? (Choose two.)

Accepted Answer

Split the data by a hash of the customer ID so that all rows for a given customer land in exactly one split.. Entity-level leakage occurs when rows from the same customer appear in more than one split, letting the model memorize customer-specific patterns and inflating validation metrics. Hashing the customer ID into split buckets and using GroupShuffleSplit with the customer ID as the group both ensure that all rows for a customer stay together, which is the correct way to partition this churn dataset.

Answer

Duplicate rows for customers in the minority class so that each split contains examples of every customer.

Answer

Sort the dataset by timestamp and take the first 80 percent of rows for training and the last 20 percent for validation.

Answer

Use a random row-level split with a fixed seed, because a fixed seed makes the split reproducible and therefore leakage-free.

Question 15

A machine learning engineer needs to split a dataset into training, validation, and test sets for a SageMaker training job. The dataset is stored in Amazon S3 as a single CSV file. The engineer wants to ensure that the splits are reproducible and that the test set is never used during training or hyperparameter tuning. Which approach should the engineer use?

Accepted Answer

Manually split the CSV file using a Python script with a fixed random seed, write the three splits to separate S3 prefixes, and use the training and validation prefixes in the training job, keeping the test prefix for final evaluation.. To ensure reproducibility and prevent data leakage, the engineer should split the data with a fixed random seed and store each split in separate S3 locations. The training job should only consume the training and validation sets, while the test set is reserved for final model evaluation. This is a standard best practice for ML workflows.

Answer

Use the SageMaker training job's built-in data splitting feature by specifying a validation split percentage in the hyperparameters.

Answer

Use SageMaker Data Wrangler to split the data into three parts and export them to S3, then use all three parts in the training job with different channels.

Answer

Use Amazon Athena to run a query that randomly assigns rows to three splits, and store the results in S3. Then use all three splits in the training job.