MLA-C01 Data Preparation for Machine Learning Practice Test 3 — 15 Questions

Question 1

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. Option C is correct because 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.

Answer

Use a bucketed join with the same number of buckets

Answer

Broadcast join the larger dataset

Answer

Use a repartition on the join key before join

Question 2

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).

Answer

One-hot encoding

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Frequency encoding

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Label encoding

Question 3

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. Log transformation is correct because it reduces skewness in numerical features by compressing the scale of large values, making the distribution more normal while preserving the original feature's interpretability (e.g., a log-transformed income value still relates to income). This is a monotonic transformation, so the order of values is maintained, and the feature remains directly understandable.

Answer

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 4

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. Option A is correct because the preprocessing function must handle missing values identically to how they were handled during training to maintain data consistency. If the training phase used mean imputation for a numeric feature, the inference pipeline must apply the same mean value; otherwise, the model will receive unexpected input distributions, degrading prediction accuracy.

Answer

Convert the data to a CSV string for model input

Answer

Re-train the model periodically using new data

Question 5

A data engineer is using AWS Glue to prepare a dataset for ML. The engineer wants to split the dataset into training and testing sets while preserving the distribution of the target variable. Which TWO methods achieve this goal? (Select TWO)

Accepted Answer

Use the `train_test_split` function from scikit-learn in a SageMaker notebook. Option B is correct because 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

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Use AWS Glue's built-in random split transform

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

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.

Answer

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.

Answer

The role does not have a trust relationship with SageMaker.

Answer

The bucket policy denies the access.

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The bucket is in a different region.

Question 8

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`.. Option B is correct because 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`.

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The `EventTimeFeatureName` is incorrectly spelled.

Question 9

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. Option B is correct because mean imputation is suitable for normally distributed data without outliers. Option A (drop rows) reduces sample size. Option C (median) is robust to outliers but not needed. Option D (mode) is for categorical data.

Answer

Impute with mode

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Impute with median

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Drop rows with missing values

Question 10

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. Option D is correct because inconsistent number of columns often results from rows with missing values where some fields are omitted. Option A (missing header) would cause a parsing error but not column count inconsistency. Option B (quoted commas) is handled by CSV parsers. Option C (delimiter) would cause consistent parsing issues.

Answer

The CSV file is missing a header row

Answer

The file uses a different delimiter like tab

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Some fields contain quoted commas

Question 11

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.

Answer

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 12

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

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Amazon Athena

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Amazon Redshift

Question 13

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

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AWS Glue Studio FindDuplicates transform

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Amazon DataZone catalog

Question 14

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.

Answer

Min-max scaling

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Log transformation

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Standardization (z-score)

Question 15

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.

Answer

Amazon S3 Select

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Amazon EMR

Answer

AWS Lambda