MLA-C01 Data Preparation for Machine Learning Practice Test 2 — 15 Questions

Question 1

An e-commerce company is building a recommendation system using user interaction data stored in Amazon DynamoDB. The data includes user_id, product_id, timestamp, event_type (click, add_to_cart, purchase), and session_id. The data science team exports the data to Amazon S3 as JSON files. During preprocessing, they discover that the 'event_type' field contains inconsistent values due to logging errors: 'Click', 'click', 'CLICK', and 'clck' all appear. Also, there are duplicate records where the same user_id, product_id, and timestamp appear multiple times with the same event_type. The team wants to use AWS Glue to clean the data for training a sequence-based recommendation model. Which set of actions should they perform?

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

Accepted Answer

Use AWS Glue to drop exact duplicate rows (all columns identical). Then apply a mapping function to standardize event_type to a controlled vocabulary (e.g., 'click', 'add_to_cart', 'purchase').. It addresses both data quality issues: first, dropping exact duplicate rows (all columns identical) removes redundant records that would bias the sequence model; second, standardizing event_type to a controlled vocabulary ensures consistent categorical input for ML training. AWS Glue's DynamicFrame with DropDuplicates and Map transformations are the appropriate tools for this ETL task.

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Use AWS Glue to group records by session_id and aggregate event_types into a list per session. Then apply a mapping function to standardize event_type names.

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Use AWS Glue to drop duplicate records based on all columns. Then drop the event_type column and use only numeric features for training.

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Use AWS Glue to impute event_type with the mode for records with inconsistent values. Then drop duplicate records based on user_id, product_id, and timestamp.

Question 2

A data scientist is using Amazon SageMaker Processing to run a feature engineering job. The job requires installing additional Python libraries not included in the default SageMaker containers. Which approach should the data scientist use to include these libraries?

Accepted Answer

Create a custom Docker image with the libraries installed and specify it in the ProcessingInput. SageMaker Processing jobs run in isolated containers that cannot install packages at runtime via pip without internet access or custom images. Creating a custom Docker image with the required libraries pre-installed ensures the environment is consistent, reproducible, and avoids dependency resolution failures during job execution. This approach aligns with SageMaker's best practice for custom dependencies.

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Add the libraries to the `requirements.txt` file in the same S3 bucket as the script

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Use Amazon EFS to store the libraries and mount them to the processing container

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Use the `pip install` command within the processing script at runtime

Question 3

A data engineer needs to join two large datasets from Amazon S3: one containing customer demographics and another containing transaction history. The join key is `customer_id`. To minimize data shuffling and improve performance, the engineer decides to use Amazon SageMaker Processing with Spark. Which configuration should the engineer use?

Accepted Answer

Use a bucketed join with the same number of buckets and co-location. Bucketed joins with the same number of buckets and co-location ensure that data with the same `customer_id` hash is physically stored together on the same nodes. This eliminates the need for expensive shuffles during the join, as Spark can perform the join locally within each executor, dramatically improving performance for large datasets in SageMaker Processing.

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Use a bucketed join with the same number of buckets

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Broadcast join the larger dataset

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Use a repartition on the join key before join

Question 4

A data scientist is preparing a dataset for a regression model that predicts house prices. The dataset includes a `neighborhood` feature with 500 distinct categories. The data scientist wants to encode this feature without increasing dimensionality too much and while capturing the target relationship. Which encoding technique should be used?

Accepted Answer

Target encoding (mean encoding). Target encoding (mean encoding) is the correct choice because it replaces each of the 500 neighborhood categories with the mean of the target variable (house price) for that category. This captures the relationship between the neighborhood and the target while adding only one new feature column, thus avoiding the massive dimensionality explosion that would occur with one-hot encoding (which would create 500 binary columns).

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One-hot encoding

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Frequency encoding

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Label encoding

Question 5

A data scientist is performing feature engineering for a dataset with both numerical and categorical features. The data scientist wants to apply transformations that preserve the interpretability of the features. Which TWO transformations should the data scientist use? (Select TWO)

Accepted Answer

Log transformation of skewed numerical features. Option A (log transformation of skewed numerical features) is correct because applying a log (e.g., natural log or log1p) to right-skewed numeric variables compresses large values and reduces skew while retaining a monotonic, one-to-one mapping, so the transformed values remain directly interpretable in terms of the original feature's scale. Option C (standard scaling of numerical features) is correct because z-score standardization (subtracting the mean and dividing by the standard deviation) is a linear transformation that preserves the relative ordering and shape of the distribution, keeping the feature interpretable as 'number of standard deviations from the mean.' Option B (target encoding) is not appropriate here because it replaces categories with target statistics, which introduces leakage risk and obscures the original categorical meaning. Option D (PCA) is not appropriate because principal components are linear combinations of the original features and are generally not interpretable as the original variables. Option E (one-hot encoding) is not appropriate because it expands a categorical feature into many binary columns, increasing dimensionality and losing the single-feature interpretability the scenario requires.

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Target encoding of high-cardinality categorical features

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PCA dimensionality reduction

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One-hot encoding of categorical features

Question 6

A data scientist runs the exhibit AWS Glue ETL job. The job fails with a Spark stage failure error. What is the most likely cause?

Accepted Answer

The data type mapping in ApplyMapping is incorrect; "value" column contains non-numeric strings that cannot be cast to double.. The Spark stage failure error in an AWS Glue ETL job is most likely caused by a data type mismatch during the ApplyMapping transformation. When the 'value' column contains non-numeric strings that cannot be cast to double, Spark throws a stage failure because it cannot complete the required type conversion, leading to task failures and job termination.

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The output path is missing.

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The S3 bucket does not exist.

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The job does not have enough memory.

Question 7

A SageMaker Processing job fails with 'Access Denied' when listing objects in an S3 bucket, despite the IAM policy shown in the exhibit. What is the most likely cause?

Accepted Answer

The policy lacks `s3:ListBucket` permission.. The error 'Access Denied' when listing objects in an S3 bucket indicates that the IAM role used by the SageMaker Processing job lacks the `s3:ListBucket` permission. This permission is required for the `ListObjectsV2` API call, which is necessary to enumerate objects in the bucket. Even if the role has `s3:GetObject` and `s3:PutObject` permissions, without `s3:ListBucket`, the job cannot list the bucket contents and will fail with an access denied error.

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The role does not have a trust relationship with SageMaker.

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The bucket policy denies the access.

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The bucket is in a different region.

Question 8

A data scientist is preparing a dataset for a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?

Accepted Answer

Impute with mean. For a numerical feature with a normal distribution and no outliers, the mean is the most appropriate imputation method because it preserves the central tendency of the data without introducing bias. In linear regression, mean imputation maintains the expected value of the feature, which is critical for unbiased coefficient estimates when data are missing completely at random (MCAR).

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Impute with mode

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Impute with median

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Drop rows with missing values

Question 9

A SageMaker Processing job fails with the error: 'Unable to parse CSV file due to inconsistent number of columns'. The data is stored as CSV in S3. What is the most likely cause?

Accepted Answer

Some rows have missing values causing fewer columns. The 'inconsistent number of columns' error in a SageMaker Processing job directly indicates that some rows in the CSV file have fewer fields than expected. SageMaker's built-in CSV parser expects a uniform number of columns per row; missing values (e.g., trailing commas omitted or blank fields not represented) cause row lengths to differ, triggering this specific parsing failure.

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The CSV file is missing a header row

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The file uses a different delimiter like tab

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Some fields contain quoted commas

Question 10

A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?

Accepted Answer

Target encoding with smoothing using SageMaker Data Wrangler. Target encoding with smoothing in SageMaker Data Wrangler is the most scalable and effective approach because it replaces each high-cardinality category with the mean of the target variable, smoothed by a global prior to prevent overfitting. SageMaker Data Wrangler handles datasets with over 1000 unique values efficiently without exploding feature dimensions, unlike one-hot encoding, and avoids the ordinal bias of label encoding.

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Hash encoding using Apache Spark on Amazon EMR

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One-hot encoding using SageMaker Processing with scikit-learn

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Label encoding using Pandas in a SageMaker notebook

Question 11

A dataset contains a numerical feature with extreme outliers. The outliers are genuine (not errors), and the ML model is a linear regression which is sensitive to outliers. Which data transformation should be applied to reduce the impact of outliers while preserving the data?

Accepted Answer

Robust scaling (median and IQR). Robust scaling uses the median and interquartile range (IQR) to center and scale the data, making it resistant to extreme outliers. Since linear regression is sensitive to outliers, this transformation reduces their influence while preserving the original data distribution, unlike methods that rely on mean and variance.

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Min-max scaling

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Log transformation

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Standardization (z-score)

Question 12

An ML engineer needs to convert a raw dataset from CSV to Parquet format in a serverless manner for cost efficiency. Which AWS service can be used to perform this conversion without managing servers?

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL service that can automatically convert CSV to Parquet using its built-in transform capabilities, such as the `ChangeSchema` or `ConvertToParquet` transforms in a Glue ETL job. This eliminates the need to provision or manage any servers, aligning with the cost-efficiency requirement.

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Amazon S3 Select

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Amazon EMR

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AWS Lambda

Question 13

In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?

Accepted Answer

Import the Redshift table directly using a Data Wrangler source step and apply a join transform. SageMaker Data Wrangler natively supports Amazon Redshift as a source via a direct connection, allowing you to import the Redshift table as a source step and then apply a join transform within the same visual flow. This approach avoids unnecessary data movement or intermediate exports, which is critical for hundreds of GB of data, as it leverages Data Wrangler's optimized in-memory and Spark-based processing to perform the join efficiently.

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Use Amazon Athena federated query to join in place and import the result

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Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join

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Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler

Question 14

A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)

Accepted Answer

Undersample the majority class. Option D (Undersample the majority class) is correct because reducing the number of majority-class examples rebalances the class ratio, so the model sees a less skewed distribution and is less biased toward the negative class. Option E (Oversample the minority class using SMOTE) is correct because SMOTE synthesizes new minority-class instances by interpolating between existing minority neighbors, increasing the positive class representation without simply duplicating records. Option A is not appropriate because removing outliers from the already scarce minority class would discard valuable positive examples and worsen the imbalance. Option B is irrelevant because PCA is a dimensionality-reduction technique that does not change the class distribution. Option C is not a balancing technique; stratified splitting only preserves the existing 5% positive ratio across train and test sets, so it does not address the imbalance itself.

Answer

Remove outliers from the minority class

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Apply PCA to reduce dimensionality

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Use stratified splitting for train/test sets

Question 15

You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)

Accepted Answer

Standardize features using parameters computed only from the training set. Option B is correct because standardization parameters such as the mean and standard deviation must be computed only from the training set and then applied to the validation/test sets; fitting a scaler on the full dataset leaks future information into the model. Option C is correct because a time-based train/test split preserves chronological order, ensuring the model is trained on earlier observations and evaluated on later ones, which mirrors real forecasting conditions. Option D is correct because feature engineering for time-series must rely solely on past data, such as lag features or rolling statistics computed with past-only windows, to avoid using future values that would not be available at prediction time. Option A is not correct because imputing missing values with the mean of the entire dataset incorporates information from the test/future period into training, causing data leakage. Option E is not correct because randomly shuffling the data before splitting destroys temporal order and allows future observations to appear in the training set, which is a classic source of leakage in time-series problems.

Answer

Impute missing values using the mean of the entire dataset

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Shuffle the data randomly before splitting