MLA-C01 Practice Test 31 — 15 Questions

Question 1

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 2

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

Accepted Answer

JSON Lines files with 'start', 'target', and optional fields per time series. DeepAR requires time series data to be provided in JSON Lines format, where each line represents a single time series with a 'start' timestamp (in ISO 8601 format), a 'target' array of values, and optional fields like 'cat' for categorical features. This structured format allows DeepAR to handle variable-length sequences and missing values natively, which is not possible with simple CSV or wide-format data.

Answer

A wide-format CSV where each column is a different time series

Answer

Parquet files with a schema containing timestamp, item_id, and value

Answer

A single CSV file with columns: timestamp, item_id, value

Question 3

A team is building a recommendation system and wants to store and serve features for online and offline models. The features include user statistics (updated daily) and movie metadata (static). The team needs low-latency inference for real-time recommendations and wants to reuse features across multiple models. Which AWS service should the team use to store, manage, and serve these features?

Accepted Answer

SageMaker Feature Store.. Amazon SageMaker Feature Store is purpose-built for storing, managing, and serving ML features with low-latency retrieval for online inference and batch serving for offline training. It supports feature reuse across multiple models by providing a centralized feature registry, consistent feature definitions, and both online (low-latency) and offline (S3-based) stores, which directly matches the team's requirements for real-time recommendations and cross-model reuse.

Answer

Amazon DynamoDB with TTL.

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AWS Glue Data Catalog.

Answer

Amazon S3 with AWS Lambda for serving.

Question 4

A data scientist is using SageMaker built-in linear learner algorithm for a regression problem. The dataset has 10 features, some have missing values, and the target variable is right-skewed. The data scientist wants to handle missing values and transform the target variable to improve model performance. Which data preparation steps should the data scientist take?

Accepted Answer

Impute missing values with the median of each feature and apply a log transformation to the target variable.. Option C is correct because imputing missing values with the median is robust to outliers and preserves the distribution of each feature, which is important when the target is right-skewed. Applying a log transformation to the right-skewed target variable helps normalize its distribution, which aligns with the linear learner algorithm's assumption of normally distributed errors and improves convergence and prediction accuracy.

Answer

Apply one-hot encoding to all features and remove missing values by dropping rows.

Answer

Standardize all features to have zero mean and unit variance, then apply a box-cox transformation to the target.

Answer

Remove rows with missing values and normalize the target to range [0,1].

Question 5

A data scientist is preparing text data for natural language processing (NLP). The corpus contains many rare words and typos. To reduce dimensionality and improve generalization, they decide to apply stemming and remove stop words. However, after training, the model performs poorly on domain-specific terms. What is the most likely cause?

Accepted Answer

Both stemming and stop word removal are inappropriate for the domain. Option B is correct because both stemming and stop word removal are inappropriate for this domain. Stemming aggressively reduces words to their root forms, which can conflate distinct domain-specific terms (e.g., 'therapy' and 'therapist' both stem to 'therap'), losing critical semantic nuance. Stop word removal can discard words that carry domain-specific meaning (e.g., 'not' in medical negation or 'up' in 'tune-up' for maintenance), leading to poor generalization on specialized vocabulary.

Answer

The corpus should be lemmatized instead

Answer

Stemming is too aggressive for the domain

Answer

Stop word removal removed important context words

Question 6

Refer to the exhibit. A data engineer runs a Glue ETL job that uses a Python script. The job fails because of a missing module `scikit-learn`. Which fix is MOST appropriate?

Accepted Answer

Use a Glue Python shell job instead. Option D is correct because a Glue Python shell job includes pre-installed libraries like scikit-learn, eliminating the missing module error without additional configuration. This job type is designed for lightweight Python scripts that do not require the distributed processing of Spark, making it the most appropriate fix for a simple dependency issue.

Answer

Modify the script to install scikit-learn using pip at runtime

Answer

Add a --additional-python-modules argument to the job with scikit-learn

Answer

Switch to a Glue job using Spark instead of Python

Question 7

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

Answer

Encode categorical features

Question 8

A marketing company is preparing a dataset to train a logistic regression model to predict whether a customer will click on an online ad. The dataset includes 1 million records with features: customer_age (numeric), income (numeric), education_level (ordinal: high school, bachelor, master, PhD), and ad_category (categorical: 50 unique values). The data is stored in a CSV file in Amazon S3. The data scientist plans to use Amazon SageMaker's built-in linear learner algorithm. The data scientist needs to preprocess the data before training. What is the correct sequence of data preparation steps that should be applied to this dataset to ensure optimal model performance?

Accepted Answer

Standardize customer_age and income to have zero mean and unit variance, one-hot encode ad_category, ordinal encode education_level (e.g., map to 1-4), then combine all features into a feature matrix.. Option D is correct because it applies appropriate preprocessing for a logistic regression model using SageMaker's linear learner. Standardizing numeric features (zero mean, unit variance) is essential for linear models to ensure convergence and equal feature influence. One-hot encoding the categorical ad_category (50 unique values) avoids imposing ordinal relationships, while ordinal encoding education_level respects its natural order. This combination prepares a feature matrix suitable for the linear learner's optimization.

Answer

Drop any duplicate records, apply min-max scaling to all numeric features, and use target encoding for ad_category based on click rates.

Answer

Apply PCA to all numeric and categorical features after converting categories to numeric indices, then standardize the principal components.

Answer

Apply min-max scaling to customer_age and income, label encode education_level and ad_category, then use recursive feature elimination to reduce dimensionality.

Question 9

A company uses Amazon SageMaker Ground Truth to create labeled datasets for object detection. The output must be in COCO format for downstream model training. How should the data preparation process be configured?

Accepted Answer

Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration. Option D is correct because Amazon SageMaker Ground Truth natively supports outputting object detection labeling jobs in COCO format. When you select 'Object Detection' as the task type, the labeling job configuration includes an option to specify 'COCO' as the output format, which automatically structures the labeled data into the required COCO JSON schema without any post-processing.

Answer

Use a built-in transformation to convert from Ground Truth JSON to COCO after labeling

Answer

Use a pre-built AWS Lambda function to transform annotations to COCO

Answer

Write a custom SageMaker Processing script to convert the output to COCO

Question 10

A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?

Accepted Answer

Use AWS Glue to create a crawler and ETL job that converts CSV to Parquet.. AWS Glue is the correct choice because it provides a serverless, pay-per-use ETL service that can automatically convert CSV to Parquet without writing custom code. The Glue crawler infers the schema, and the ETL job uses built-in transforms to efficiently handle 10 TB of data with minimal cost, as it only charges for the resources consumed during the job execution.

Answer

Use SageMaker Processing with a TensorFlow script to read CSV and write Parquet.

Answer

Use Amazon S3 Select to convert the data to Parquet during retrieval.

Answer

Use Amazon EMR with a Spark job to convert the files.

Question 11

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

Answer

The policy does not specify the bucket ARN without /*

Answer

The Glue job role does not have the required permissions

Question 12

A machine learning engineer is using SageMaker Data Wrangler to perform data validation. Which step should be added to the pipeline to ensure data quality before training?

Accepted Answer

Apply a 'Data Quality' transformation in Data Wrangler to validate column statistics. Option B is correct because SageMaker Data Wrangler includes a built-in 'Data Quality' transformation that allows you to validate column statistics (e.g., missing values, min/max, distinct counts) directly within the visual pipeline. This step ensures data quality without requiring custom code or external services, integrating seamlessly with the Data Wrangler workflow for pre-training validation.

Answer

Write a custom SageMaker Processing job for validation

Answer

Use AWS Glue DataBrew to profile the dataset

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Add a SageMaker Pipeline step to check data quality after Data Wrangler

Question 13

A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.

Answer

Oversampling the minority class by duplicating examples

Answer

Collect more data to match the number of samples in both classes

Answer

Random undersampling of the majority class

Question 14

A data science team needs to deploy a PyTorch model for real-time inference with low latency. The model requires GPU acceleration. Which SageMaker endpoint configuration should they use?

Accepted Answer

Create a real-time endpoint using an ml.p3.2xlarge instance. Option D is correct because real-time SageMaker endpoints with GPU instances like ml.p3.2xlarge are specifically designed for low-latency, synchronous inference with GPU acceleration. PyTorch models requiring GPU must use instance types that support NVIDIA CUDA, and the ml.p3 family provides the necessary GPU compute for real-time predictions.

Answer

Create a multi-model endpoint using ml.m5.large instances

Answer

Create a serverless endpoint with memory set to 6144 MB

Answer

Create a batch transform job using an ml.c5.xlarge instance

Question 15

Refer to the exhibit. A SageMaker Processing job fails with the following error log. Which change during data preparation would resolve the issue?

Accepted Answer

Modify the preprocessing script to cast 'age' to float using astype(float). Option D is correct because the error log indicates a type mismatch when processing the 'age' column, likely due to mixed data types (e.g., strings and numbers) in a column expected to be numeric. By explicitly casting the column to float using astype(float) in the preprocessing script, you ensure consistent numeric type handling, which resolves the failure during SageMaker Processing job execution.

Answer

In SageMaker Data Wrangler, set the 'age' column type to 'number'

Answer

Drop rows with missing values in the 'age' column before training

Answer

Remove the 'age' column from the dataset entirely