MLA-C01 Practice Test 10 — 10 Questions

Question 1

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 2

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

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Add each partition manually using ALTER TABLE ADD PARTITION

Question 3

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

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Remove stop words

Question 4

A data scientist is using Amazon SageMaker Data Wrangler for feature engineering on a large dataset stored in S3. The dataset has a column 'ProductCategory' with 1000+ unique values. To reduce dimensionality, they want to group categories that appear less than 1% of the time into an 'Other' category. Which Data Wrangler transform should they use?

Accepted Answer

Handle rare values. The 'Handle rare values' transform in SageMaker Data Wrangler is specifically designed to group infrequent category values into a single 'Other' bucket based on a frequency threshold (e.g., less than 1%). This directly addresses the need to reduce dimensionality by consolidating rare categories without requiring custom code or manual grouping.

Answer

Group similar categories

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Custom transform with Python

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One-hot encode with threshold

Question 5

A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?

Accepted Answer

The pipeline can be configured to fail if data quality checks do not meet thresholds. Option A is correct because SageMaker Pipelines allows you to define conditions that evaluate the output of data quality checks (e.g., using Amazon SageMaker Model Monitor or custom validation scripts). If the checks fail to meet specified thresholds (e.g., missing values exceed 5%), the pipeline can be configured to fail, stopping execution and preventing downstream steps from processing invalid data.

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Data validation can only be performed on training data, not inference data

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Data validation steps cannot pass results to subsequent steps

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Data validation requires a trained model to evaluate predictions

Question 6

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 rows and 200 features, with 5% positive class. The engineer suspects class imbalance may affect model performance. Which TWO actions should the engineer take to mitigate imbalance? (Choose 2.)

Accepted Answer

Apply SMOTE only to training data. Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the class distribution. Applying SMOTE only to the training data is critical to avoid data leakage, as the test set must remain untouched to provide an unbiased evaluation of model performance on the original class distribution.

Answer

Perform PCA to reduce dimensions

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Remove features with low variance

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Use k-fold cross-validation

Question 7

A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The source data in S3 has a schema that evolves over time (new columns are added occasionally). The Glue job schema is defined as a fixed schema in the job script. After an update to the source data, the Glue job fails with an error about mismatched schemas. How should the data engineer modify the data preparation process to handle schema evolution?

Accepted Answer

Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns. Option A is correct because AWS Glue DynamicFrames natively handle schema evolution by allowing you to apply a mapping that can include new columns. By using `applyMapping` with `resolveChoice`, you can define how to handle new fields (e.g., cast to a type or keep as a struct), preventing job failures when the source schema changes. This avoids the rigidity of a fixed schema in the job script.

Answer

Run a Glue crawler before each job to update the Data Catalog, but keep the fixed schema in the job

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Store the schema definition in a separate file in S3 and read it at runtime

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Manually update the Glue job script each time the schema changes

Question 8

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.

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Remove all outliers from the dataset.

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

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

Question 9

A company is building a fraud detection model on an imbalanced dataset (99% legitimate, 1% fraudulent). To improve recall on the minority class, they want to resample data. Which combination of techniques should they use?

Accepted Answer

SMOTE on training set only. SMOTE should be applied only to the training set to avoid data leakage; evaluation must reflect the original distribution. Random undersampling may discard useful majority samples; random oversampling before split leaks information.

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SMOTE on entire dataset before train/test split

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Random oversampling of minority class before train/test split

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Random undersampling of majority class

Question 10

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

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Use the DistinctRows transform in Amazon SageMaker Data Wrangler

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Use the pandas drop_duplicates() method in a SageMaker notebook