MLA-C01 Data Preparation for Machine Learning Practice Test 10 — 15 Questions

Question 1

A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?

Accepted Answer

Partition the data by a frequently filtered column, such as date. Option A is correct because partitioning the S3 data by a frequently filtered column such as date lets Athena prune partitions and scan only the relevant prefixes, drastically reducing the amount of data read and thus improving performance and lowering cost. Option D is correct because columnar formats like Parquet or ORC allow Athena to read only the columns referenced by a query and provide better compression and predicate pushdown, minimizing I/O compared to row-based formats. Option E is correct because compressing data with Snappy or gzip reduces the bytes transferred from S3 and read by Athena, and these splittable/columnar-friendly codecs work well with Parquet/ORC to further speed up scans. Option B is incorrect because uncompressed CSV is a row-based, non-splittable-friendly format that forces Athena to scan all columns and more bytes, hurting performance. Option C is incorrect because partitioning by every column creates an excessive number of small partitions and files, which increases metadata overhead and can degrade rather than improve query performance.

Answer

Use uncompressed CSV files for simplicity

Answer

Partition the data by every column to maximize filtering

Question 2

A data scientist is preparing a large dataset (50 GB) for training a TensorFlow model on SageMaker. The dataset consists of many small CSV files. Training is slow due to I/O bottlenecks. Which data preparation strategy most effectively accelerates training?

Accepted Answer

Convert the dataset to TFRecord format and use tf.data pipeline with prefetching. TFRecord format stores data in a binary, row-oriented format that TensorFlow's tf.data API can read efficiently, especially with prefetching to overlap data loading with model computation. This eliminates the per-file open/parse overhead of many small CSV files, which is the primary cause of I/O bottlenecks in this scenario.

Answer

Convert the dataset to Parquet format and use Apache Arrow for loading

Answer

Compress the CSV files and decompress during data loading

Answer

Use a larger instance type with more vCPUs

Question 3

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

Accepted Answer

JSON Lines files with 'start', 'target', and optional fields per time series. DeepAR requires time series data to be provided in JSON Lines format, where each line represents a single time series with a 'start' timestamp (in ISO 8601 format), a 'target' array of values, and optional fields like 'cat' for categorical features. This structured format allows DeepAR to handle variable-length sequences and missing values natively, which is not possible with simple CSV or wide-format data.

Answer

A wide-format CSV where each column is a different time series

Answer

Parquet files with a schema containing timestamp, item_id, and value

Answer

A single CSV file with columns: timestamp, item_id, value

Question 4

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 5

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.. 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 6

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 bias or break training algorithms, so best practice is to detect them (e.g., with pandas isnull() or Amazon SageMaker Data Wrangler) and handle them via imputation, removal, or model-native handling. Option D is correct because splitting data into training, validation, and test sets lets you fit parameters, tune hyperparameters, and estimate generalization performance on unseen data, avoiding overfitting and data leakage. Option A is not recommended because not all outliers are errors; blindly removing them can discard legitimate signal and distort the distribution. Option B is wrong because training on the entire dataset leaves no held-out data for validation or unbiased testing. Option E is wrong because normalization is not always required and [0,1] scaling is only one of several techniques (e.g., standardization, log transforms) chosen based on the algorithm and feature distribution.

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 7

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.. 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 8

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'.. 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 9

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.. 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.

Question 10

A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?

Accepted Answer

Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.. AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.

Answer

Use a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.

Answer

Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.

Answer

Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.

Question 11

A retail company is building a machine learning model to predict customer churn. The data engineering team has extracted customer transaction data from Amazon Aurora and stored it as CSV files in Amazon S3. The data includes customer IDs, transaction amounts, timestamps, and product categories. A data scientist discovers that the dataset contains several missing values in the 'transaction_amount' column for about 15% of the records. The data scientist also notices that the 'customer_id' column has some duplicate entries. The team wants to prepare the data for training a churn model using Amazon SageMaker. The data is approximately 50 GB in size. What should the data scientist do to handle the missing values and duplicates efficiently while preparing the data for training?

Accepted Answer

Use an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3.. AWS Glue ETL jobs are serverless and designed to handle large-scale data transformations (like 50 GB) without requiring manual cluster management. Glue can read CSV files from S3, apply transformations to impute missing values with the mean or median, drop duplicate customer IDs, and write the cleaned data back to S3, all while scaling automatically to handle the data volume efficiently.

Answer

Use a SageMaker notebook instance with Pandas to load the entire dataset into memory, fill missing values with the median, and drop duplicate customer IDs.

Answer

Drop all records with missing values in the transaction_amount column and remove duplicate customer IDs using an Athena SQL query, then store the result in S3.

Answer

Use an Amazon EMR cluster with Spark to read the CSV files, impute missing transaction amounts with the mean or median, and remove duplicate customers.

Question 12

A social media company is processing a real-time stream of user activity data from Amazon Kinesis Data Streams to train a machine learning model for content recommendation. The raw data includes user ID, timestamp, content ID, interaction type (like, share, comment), and device type. The data scientists need to aggregate features per user over a sliding window of 7 days, including counts of interaction types, unique content IDs engaged, and a moving average of interaction timestamps. The aggregated data will be used to update a user embedding model. The streaming data volume is approximately 500 records per second, and the company uses an AWS Glue streaming ETL job for transformation. However, the Glue job is failing frequently with high latency and checkpoint errors. The team needs a more robust solution to prepare the streaming data features. Which approach should the team take?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB.. Amazon Kinesis Data Analytics for Apache Flink provides native support for sliding window aggregations with managed state and exactly-once processing semantics, which directly addresses the high latency and checkpoint errors seen in the Glue streaming ETL job. Flink's checkpointing mechanism ensures fault-tolerant state management for the 7-day sliding window, while Glue's Spark Streaming engine struggles with long-running stateful operations at 500 records/sec due to its micro-batch architecture and checkpoint overhead.

Answer

Increase the DPU count on the Glue streaming ETL job and reduce the checkpoint interval to improve performance.

Answer

Use AWS Lambda functions to process records from Kinesis, store intermediate aggregation results in Amazon DynamoDB, and read them back to compute windowed features.

Answer

Use Amazon SageMaker Processing jobs that run periodically every hour to read data from S3 (landing from Kinesis Firehose) and perform the aggregations batch-wise.

Question 13

A data engineer is building an AWS Glue ETL job that reads raw JSON clickstream events from Amazon S3, flattens nested structures, and writes Parquet to a curated S3 prefix for SageMaker training. The job must run daily on only the newly arrived files and must keep the Glue Data Catalog table current so Athena and SageMaker can query it. Which TWO configurations should the engineer apply to meet these requirements? (Choose two.)

Accepted Answer

Enable job bookmarks on the Glue ETL job.. Job bookmarks give Glue the persistent state needed to process only newly arrived S3 objects, which is exactly the incremental requirement. Keeping the Data Catalog current, either through the job's catalog update option or a crawler over the curated prefix, ensures Athena and SageMaker see the latest schema and partitions. Together these two settings make the daily pipeline both efficient and queryable.

Answer

Convert the input JSON to CSV before processing to improve Glue read performance.

Answer

Set the job's maximum capacity to the smallest possible value to reduce cost.

Answer

Use a Development endpoint to run the daily job interactively.

Question 14

A data scientist has a 40 GB CSV dataset in Amazon S3 that will be used to train a SageMaker model. The training script reads the data with pandas, and the scientist wants to reduce both storage cost and training-time I/O without changing the logical schema. Which data preparation action should be taken?

Accepted Answer

Convert the dataset to Parquet with Snappy compression and keep the same columns.. Parquet stores data by column and compresses each column efficiently, so a training script that needs a subset of columns reads far fewer bytes than it would from CSV. Snappy compression further reduces the S3 object size, lowering storage cost. Because the column names and types stay the same, the logical schema is unchanged and the pandas-based script can read the Parquet files with minimal modification.

Answer

Move the dataset to Amazon EFS and mount it to the training container.

Answer

Convert the dataset to JSON Lines and enable S3 Transfer Acceleration.

Answer

Split the CSV into many smaller CSV files and keep the same column layout.

Question 15

A data engineer needs to prepare a dataset for a fraud detection model. The dataset contains a highly skewed numerical feature with extreme outliers. The engineer decides to apply a logarithmic transformation to this feature before training. Which SageMaker Data Wrangler transform should be used to apply the logarithmic transformation?

Accepted Answer

Use the 'Log Transform' transform in Data Wrangler.. The logarithmic transformation is a common technique to reduce right skewness and stabilize variance in numerical data. In SageMaker Data Wrangler, the 'Log Transform' transform applies a natural logarithm to the selected column, effectively compressing the scale of large values and making the distribution more symmetric. This helps models that are sensitive to feature distributions, such as linear models, to perform better. Other transforms like scaling or encoding do not change the distribution shape.

Answer

Use the 'Standard Scaler' transform in Data Wrangler.

Answer

Use the 'One-Hot Encoding' transform in Data Wrangler.

Answer

Use the 'Min-Max Scaler' transform in Data Wrangler.