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

Question 1

A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)

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

Accepted Answer

Use AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.. Option D is correct because AWS Glue DynamicFrames run on Apache Spark, which scales horizontally across a cluster, and computing the interquartile range (IQR) with Spark SQL/DataFrame operations lets the engineer detect and filter outliers (e.g., values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR) across large datasets efficiently. Option E is correct because Amazon SageMaker Data Wrangler provides built-in outlier detection transforms (such as Robust Standard Deviation, Standard Deviation, and Quantile-based detection) that can be applied to a dataset at scale and exported into a processing or training pipeline, fitting the ML preparation workflow. Option A is not appropriate because manually inspecting data in Amazon S3 does not scale and is error-prone for large datasets. Option B is not appropriate because training a neural network just to remove outliers is overkill, costly, and unnecessary when statistical methods suffice. Option C is not appropriate because pandas in a SageMaker notebook operates in memory on a single instance, so it does not scale to large datasets the way Spark or Data Wrangler do.

Answer

Manually remove outliers by inspecting the data in Amazon S3.

Answer

Train a neural network to identify anomalies and remove them.

Answer

Use pandas in a SageMaker notebook to calculate z-scores and filter outliers.

Question 2

A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?

Accepted Answer

Log transformation (natural log). A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.

Answer

Standardization (z-score)

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Min-max scaling to [0,1]

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Equal-width binning

Question 3

A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

Accepted Answer

Read the Parquet files directly using SparkSession.read.parquet. SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

Answer

Read the Parquet files as text using SparkContext.textFile

Answer

Split the dataset into many small Parquet files (e.g., 1 MB each)

Answer

Convert the Parquet files to CSV before processing

Question 4

A team is preparing text data for a natural language processing (NLP) model. They have a corpus of customer reviews. Which THREE preprocessing steps are essential to reduce noise and improve model performance?

Accepted Answer

Remove punctuation and special characters. Option B is correct because removing punctuation and special characters eliminates non-linguistic symbols that add noise and are typically not useful features for NLP models, helping normalize the token stream. Option D is correct because stemming or lemmatization reduces inflected words to their base or dictionary form (e.g., 'running' to 'run'), decreasing vocabulary size and helping the model generalize across morphological variants. Option E is correct because converting all text to lowercase ensures that words like 'Review' and 'review' are treated as the same token, preventing spurious vocabulary duplication and improving consistency. Option A is not a noise-reduction preprocessing step; one-hot encoding is a feature representation technique that actually increases dimensionality and does not clean the text. Option C is also not a preprocessing cleaning step; TF-IDF is a numerical vectorization/weighting method applied after text has already been normalized and tokenized.

Answer

Apply one-hot encoding to each word

Answer

Compute TF-IDF vectors

Question 5

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

Accepted Answer

Random oversampling of the minority class. Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.

Answer

Principal Component Analysis (PCA) dimensionality reduction

Answer

Standard scaling of numerical features

Answer

One-hot encoding of categorical variables

Question 6

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

Accepted Answer

Word embeddings (e.g., Word2Vec or GloVe). Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

Answer

One-hot encoding of each word

Answer

Bag-of-words with TF-IDF

Answer

Hashing vectorizer

Question 7

A company uses Amazon SageMaker Data Wrangler to prepare data for ML. The dataset contains a timestamp column and sensor readings from IoT devices. The data scientist needs to create features such as moving averages and rolling statistics over time windows. Which Data Wrangler transformation type should be selected?

Accepted Answer

Window function. Window functions in Amazon SageMaker Data Wrangler allow you to compute moving averages, rolling statistics, and other time-window-based aggregations over ordered partitions of data. This is the correct transformation type because it directly supports operations like `SUM() OVER (ORDER BY timestamp ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)` without requiring custom code or losing row-level granularity.

Answer

Join

Answer

Custom Python script

Answer

Group by and aggregate

Question 8

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

Accepted Answer

Parquet. Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.

Answer

XML

Answer

ORC

Answer

JSON Lines

Question 9

A machine learning team is building a model to predict customer churn. They have historical data that includes customer activity logs, each with a timestamp. The team wants to ensure that the training data does not contain any data leakage from the future. Which approach should they take when preparing the training and validation datasets?

Accepted Answer

Split the data by time, using data before a certain date for training and after for validation. Splitting by time (chronological split) prevents data leakage by ensuring that the validation set contains only future data relative to the training set. In time-series or timestamped data, random splits can allow the model to learn from future patterns, artificially inflating performance. This approach respects the temporal dependency inherent in customer churn prediction.

Answer

Use stratified sampling based on churn label

Answer

Randomly split the data 80/20 for training and validation

Answer

Use k-fold cross-validation with shuffling

Question 10

A data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?

Accepted Answer

Handle missing values (with median strategy). Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing custom code.

Answer

Replace missing with constant

Answer

Custom transform with Python

Answer

Drop missing rows

Question 11

A company is building a real-time inference pipeline for an ML model. The raw data arrives in JSON format via Amazon Kinesis Data Streams. Before invoking the SageMaker endpoint, the data must be preprocessed to match the training data format. Which THREE steps should be included in the preprocessing function? (Select THREE)

Accepted Answer

Ensure that missing values are handled consistently with the training phase. The preprocessing function must first parse the JSON payload (E), because the raw records arrive from Kinesis Data Streams in JSON format and the individual feature fields cannot be accessed until the JSON is decoded into a usable structure. It must also apply the same feature engineering transformations used during training (C), such as scaling and encoding, since the SageMaker endpoint expects inputs in the exact distribution and representation the model learned; using different transformations would cause training-serving skew and degrade predictions. Missing values must be handled consistently with the training phase (A), because imputation or drop logic applied at inference must mirror training-time behavior to keep the feature semantics identical. Option B is not required because the model's input format is not stated to be CSV, and forcing a CSV string could conflict with the actual serialization the endpoint expects. Option D is incorrect because retraining the model is a separate MLOps activity, not part of the per-record preprocessing function invoked before calling the endpoint.

Answer

Convert the data to a CSV string for model input

Answer

Re-train the model periodically using new data

Question 12

A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)

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

Accepted Answer

Use the `train_test_split` function from scikit-learn in a SageMaker notebook. The `train_test_split` function from scikit-learn supports the `stratify` parameter, which preserves the distribution of the target variable when splitting a dataset into training and testing sets. This is a standard, reliable method for stratified splitting in Python-based ML workflows, and it can be used directly in a SageMaker notebook.

Answer

Use Amazon Athena to create views with random sampling

Answer

Use AWS Glue's built-in random split transform

Answer

Use Amazon SageMaker's built-in SplitType parameter in a Processing Job

Question 13

A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?

Accepted Answer

The `age` feature type should be `Integral`, not `String`.. The feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.

Answer

The role does not have permissions to write to the feature store.

Answer

The `OnlineStoreConfig` must include a `SecurityConfig`.

Answer

The `EventTimeFeatureName` is incorrectly spelled.

Question 14

An organization stores raw data in Amazon S3 as CSV files. They need to perform serverless data transformation and convert the data to Parquet format for efficient ML training. Which AWS service is most appropriate?

Accepted Answer

AWS Glue. AWS Glue is the most appropriate service because it is a fully managed, serverless ETL service designed specifically for data transformation tasks like converting CSV to Parquet. It automatically handles schema inference, data partitioning, and optimization for ML training workloads without requiring infrastructure management.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon Redshift

Question 15

A data scientist is using SageMaker Data Wrangler to prepare a large dataset. The data contains duplicate rows, which could bias the model. Which built-in step in Data Wrangler can automatically detect and remove duplicates?

Accepted Answer

Handle Duplicates transform in Data Wrangler. The Handle Duplicates transform is a built-in step in SageMaker Data Wrangler specifically designed to detect and remove duplicate rows from a dataset. It provides configurable options such as selecting a subset of columns for duplicate detection and choosing whether to keep the first or last occurrence, directly addressing the bias risk from duplicate rows in ML training data.

Answer

Amazon QuickSight duplicate detection

Answer

AWS Glue Studio FindDuplicates transform

Answer

Amazon DataZone catalog