MLA-C01 Data Preparation for Machine Learning Practice Test 6 — 15 Questions

Question 1

A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)

Accepted Answer

Use Python's json.loads in a map function. Option A is correct because Python's json.loads can be used within a PySpark map function to parse nested JSON strings from streaming data in AWS Glue ETL. This allows you to extract and flatten nested fields into a tabular structure by iterating over each record and converting the JSON into a flat dictionary, which can then be mapped to DataFrame columns.

Answer

Use Athena's UNNEST function on the raw data

Answer

Use Amazon SageMaker Processing with scikit-learn

Question 2

A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)

Accepted Answer

Standardize numeric features. Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.

Answer

Normalize text data to lowercase

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Remove all outliers blindly

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Use a large neural network to handle all transformations

Question 3

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 records with 100 features. The target variable is imbalanced, with 95% negative class and 5% positive class. Which data preparation step should the data scientist take to address the imbalance before training?

Accepted Answer

Apply SMOTE to oversample the positive class. Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class (positive class, 5%) by interpolating between existing minority instances. This addresses the severe class imbalance (95:5) without discarding data, allowing the model to learn decision boundaries for the minority class more effectively than simple duplication.

Answer

Normalize all features to a 0-1 range

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Use cross-validation to handle imbalance

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Remove enough instances of the negative class to achieve balance

Question 4

A company is building a machine learning model on customer transaction data stored in Amazon S3. The data includes columns with missing values in the 'age' field. The data scientist wants to impute missing values with the median age across all customers. Which approach is MOST efficient for preparing the data at scale?

Accepted Answer

Use a custom PySpark script in AWS Glue to compute median and fill missing values. Option C is correct because AWS Glue with PySpark provides a distributed, scalable environment that can efficiently compute the median and fill missing values across large datasets stored in S3. PySpark's DataFrame API handles the median computation natively, and the Glue job runs on a managed Spark cluster, making it the most efficient approach for data preparation at scale without moving data out of the AWS ecosystem.

Answer

Use AWS Glue Transform with the FillMissingValues transform specifying the median strategy

Answer

Use a custom Python script with pandas to compute median and fill missing values, then upload to S3

Answer

Use Amazon Athena SQL query to compute median and update the table

Question 5

A machine learning team is processing a large dataset in Amazon SageMaker using a processing job. The data is stored in S3 in CSV format. The team wants to split the data into training, validation, and test sets (70/20/10) while ensuring that the distribution of a categorical feature 'region' is preserved across splits. Which SageMaker SDK method should they use to write the output?

Accepted Answer

Use sagemaker.sklearn.processing.SKLearnProcessor with a script that uses sklearn's StratifiedShuffleSplit. Option A is correct because `SKLearnProcessor` allows you to run a custom Python script that uses `sklearn.model_selection.StratifiedShuffleSplit`, which preserves the distribution of the categorical 'region' feature across the training, validation, and test splits. This is the only option that directly supports stratified splitting within a SageMaker processing job, ensuring the 70/20/10 ratio while maintaining class balance.

Answer

Use sagemaker.xgboost.processing.XGBoostProcessor with a script that uses random split

Answer

Use sagemaker.processing.Processor.run() with a custom script that uses train_test_split

Answer

Use sagemaker.processing.FrameworkProcessor with a script that uses pandas.sample

Question 6

A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?

Accepted Answer

AWS Glue Studio with a visual job. AWS Glue Studio with a visual job is the correct choice because it provides a no-code, drag-and-drop interface to create ETL jobs that can read JSON from S3 and write it as Parquet, with built-in schema inference and transformation capabilities. This minimizes coding effort while leveraging Glue's serverless Spark engine for efficient conversion, making it ideal for preparing data for Athena queries.

Answer

Amazon SageMaker Processing

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Amazon EMR

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AWS Lambda

Question 7

A data scientist is exploring data stored in an Amazon Redshift cluster. The data includes timestamp columns with different formats. The scientist wants to create a new column that standardizes the timestamp format to UTC. Which approach is MOST efficient?

Accepted Answer

Use a SELECT with CONVERT_TIMEZONE in Redshift and export to S3. Option B is correct because `CONVERT_TIMEZONE` in Amazon Redshift is a native SQL function that directly converts timestamps to UTC without moving data outside the cluster. This approach avoids the overhead of external services, leverages Redshift's massively parallel processing (MPP) engine, and is the most efficient for in-database transformations.

Answer

Use AWS Glue to read the Redshift table and apply a custom transform

Answer

Use a SageMaker notebook to query Redshift and transform

Answer

Use Amazon QuickSight to transform the timestamp

Question 8

A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?

Accepted Answer

Relationalize transform. The Relationalize transform is specifically designed to flatten nested JSON or semi-structured fields into a relational structure, making it ideal for converting complex streaming data from Kinesis into flat columns for ML training. It automatically handles arrays and structs by creating separate tables or columns, which is exactly what the team needs for downstream processing.

Answer

Spigot transform

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ResolveChoice transform

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ApplyMapping transform

Question 9

A data engineer notices that an AWS Glue ETL job is failing with an Out of Memory error when processing a large dataset. The dataset is 500 GB in size, and the worker type is G.1X. Which change is MOST likely to resolve the issue?

Accepted Answer

Use a larger worker type like G.2X. The G.1X worker type provides 16 GB of memory per worker. A 500 GB dataset requires sufficient aggregate memory across workers for processing. Increasing the worker type to G.2X (which doubles memory to 32 GB per worker) increases the memory per executor, allowing each task to handle larger data partitions without running out of memory. This directly addresses the Out of Memory error by providing more heap space for Spark operations.

Answer

Partition the input data into smaller files

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Use a Spark DataFrame instead of RDD

Answer

Increase the number of workers

Question 10

A company needs to anonymize personally identifiable information (PII) in a dataset before using it for ML. The dataset is stored in S3 as CSV files. The team wants to mask credit card numbers by replacing all digits except the last four with asterisks. Which approach is the most scalable?

Accepted Answer

Use a custom script in AWS Glue Python shell job with regex. Option B is correct because AWS Glue Python shell jobs provide a lightweight, serverless environment for running Python scripts that can efficiently process CSV files from S3 using regex-based masking. This approach scales horizontally by leveraging Glue's managed infrastructure without the overhead of Spark or additional services, making it the most scalable for simple row-wise transformations like masking credit card numbers.

Answer

Use Amazon Comprehend to detect PII and then a custom script to mask

Answer

Use AWS Glue with a custom PySpark UDF to apply regex masking

Question 11

A data scientist is preparing data for a regression model. The target variable has a skewed distribution. The scientist wants to apply a log transformation to make it closer to normal. Which step should be taken before applying log transformation?

Accepted Answer

Ensure all values are positive. The log transformation is defined only for positive real numbers; applying it to zero or negative values results in undefined or complex outputs. Therefore, before applying a log transformation, you must ensure all values in the target variable are positive, typically by adding a constant (e.g., log(x + 1)) if zeros are present. This step is a fundamental data preparation requirement for log transformations in regression modeling.

Answer

Standardize the data to zero mean and unit variance

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Remove outliers using IQR

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Center the data by subtracting the mean

Question 12

A data engineer is preparing a dataset for a classification model. The dataset contains duplicate rows. Which TWO approaches are appropriate to handle duplicates in AWS? (Choose 2.)

Accepted Answer

Use the DropDuplicates transform in AWS Glue. Option C is correct because AWS Glue provides a DropDuplicates transform within its DynamicFrame API, which is designed for ETL operations on large-scale datasets. This transform efficiently removes duplicate rows by comparing all columns or a specified subset, making it a native and scalable solution for deduplication in AWS.

Answer

Use the RemoveDuplicates built-in feature in Amazon QuickSight

Answer

Use the DistinctRows transform in Amazon SageMaker Data Wrangler

Answer

Use the pandas drop_duplicates() method in a SageMaker notebook

Question 13

A data scientist is cleaning a text dataset for natural language processing. The raw data contains HTML tags, URLs, and special characters. Which THREE steps should be taken to preprocess the text data? (Choose 3.)

Accepted Answer

Convert all text to lowercase. Converting all text to lowercase (Option A) is a standard text normalization step in NLP preprocessing. It reduces the vocabulary size by treating words like 'Apple' and 'apple' as the same token, which helps downstream models avoid treating case variations as distinct features. This is typically done early in the pipeline before tokenization or vectorization.

Answer

Encode the text using one-hot encoding

Answer

Remove stop words

Question 14

A company is preparing data for a time-series forecasting model. The data is collected from IoT sensors at irregular intervals. Which TWO steps are necessary to prepare the data? (Choose 2.)

Accepted Answer

Resample the data to a fixed frequency. Time-series forecasting models require data at consistent time intervals to capture temporal patterns and seasonality. Resampling the irregular IoT sensor data to a fixed frequency (e.g., every 5 minutes) creates a uniform time index, which is essential for algorithms like ARIMA, Prophet, or LSTM. This step ensures the model can learn from a structured sequence rather than being confused by variable time gaps.

Answer

Normalize the data to a 0-1 range

Answer

Remove outlier data points

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Encode categorical features

Question 15

Refer to the exhibit. A data scientist is trying to use AWS Glue to read data from the S3 bucket `ml-data-bucket`. The Glue job fails with an access denied error. What is the most likely cause?

Accepted Answer

The policy does not include s3:ListBucket permission on the bucket. The error occurs because the IAM policy attached to the Glue job role grants s3:GetObject on the bucket objects (via the `arn:aws:s3:::ml-data-bucket/*` resource) but does not include the s3:ListBucket permission on the bucket itself (`arn:aws:s3:::ml-data-bucket`). When AWS Glue reads data from S3, it first performs a ListBucket operation to enumerate objects in the bucket or prefix, and without that permission, the request is denied even if GetObject is allowed.

Answer

The policy allows s3:PutObject but the job only reads

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The policy does not specify the bucket ARN without /*

Answer

The Glue job role does not have the required permissions