MLA-C01 Practice Test 5 — 25 Questions

Question 1

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

Answer

Custom transform with Python

Answer

One-hot encode with threshold

Question 2

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.

Answer

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

Answer

Data validation steps cannot pass results to subsequent steps

Answer

Data validation requires a trained model to evaluate predictions

Question 3

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

Answer

Remove features with low variance

Answer

Use k-fold cross-validation

Question 4

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

Answer

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

Answer

Manually update the Glue job script each time the schema changes

Question 5

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.

Answer

Remove all outliers from the dataset.

Answer

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

Answer

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

Question 6

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.

Answer

SMOTE on entire dataset before train/test split

Answer

Random oversampling of minority class before train/test split

Answer

Random undersampling of majority class

Question 7

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

Answer

Use the DistinctRows transform in Amazon SageMaker Data Wrangler

Answer

Use the pandas drop_duplicates() method in a SageMaker notebook

Question 8

A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?

Accepted Answer

AWS Glue Studio with a visual job. AWS Glue Studio with a visual job is the correct choice because it provides a no-code, drag-and-drop interface to create ETL jobs that can read JSON from S3 and write it as Parquet, with built-in schema inference and transformation capabilities. This minimizes coding effort while leveraging Glue's serverless Spark engine for efficient conversion, making it ideal for preparing data for Athena queries.

Answer

Amazon SageMaker Processing

Answer

Amazon EMR

Answer

AWS Lambda

Question 9

A data scientist is preparing a large dataset (50 GB) for training a TensorFlow model on SageMaker. The dataset consists of many small CSV files. Training is slow due to I/O bottlenecks. Which data preparation strategy most effectively accelerates training?

Accepted Answer

Convert the dataset to TFRecord format and use tf.data pipeline with prefetching. Option A is correct because TFRecord format stores data in a binary, row-oriented format that TensorFlow's tf.data API can read efficiently, especially with prefetching to overlap data loading with model computation. This eliminates the per-file open/parse overhead of many small CSV files, which is the primary cause of I/O bottlenecks in this scenario.

Answer

Convert the dataset to Parquet format and use Apache Arrow for loading

Answer

Compress the CSV files and decompress during data loading

Answer

Use a larger instance type with more vCPUs

Question 10

A machine learning engineer is building a regression model to predict house prices. The feature 'square_footage' has values ranging from 500 to 10,000, while 'num_bedrooms' ranges from 1 to 10. Which preprocessing step is most critical before training a model that uses gradient descent?

Accepted Answer

Standardize both features to have zero mean and unit variance.. Gradient descent is sensitive to the scale of features because it updates weights proportionally to the feature values. With 'square_footage' (500–10,000) and 'num_bedrooms' (1–10), the large range difference causes the loss function's contours to be elongated, leading to slow or unstable convergence. Standardizing both features to zero mean and unit variance ensures each feature contributes equally to the gradient updates, enabling faster and more reliable optimization.

Answer

Apply a logarithmic transformation to both features.

Answer

Encode the 'num_bedrooms' feature using one-hot encoding.

Answer

Impute missing values using the mean of the feature.

Question 11

A company uses Amazon SageMaker Data Wrangler to create a data flow for a classification model. The dataset contains a high-cardinality categorical feature 'product_id' with 50,000 unique values. The data scientist wants to reduce dimensionality while preserving predictive power. Which approach is most effective?

Accepted Answer

Perform target encoding by replacing each product ID with the average target value for that product.. Target encoding is the most effective approach for high-cardinality categorical features because it replaces each category with the mean of the target variable, preserving predictive signal while drastically reducing dimensionality. In SageMaker Data Wrangler, this can be implemented using the 'Encode categorical' transform with the 'Target encoding' option, which avoids the explosion of features caused by one-hot encoding and retains the relationship between product IDs and the target.

Answer

Apply one-hot encoding to the 'product_id' column.

Answer

Use feature hashing to map product IDs to a fixed number of buckets (e.g., 100).

Answer

Drop the 'product_id' column entirely.

Question 12

Refer to the exhibit. A data engineer runs an AWS Glue ETL job with the following script portion. The job fails with an error: 'An error occurred while calling o113.pyWriteDynamicFrame. No such file or directory'. What is the most likely cause?

Accepted Answer

The output S3 path is missing a trailing slash. The error 'No such file or directory' when calling `pyWriteDynamicFrame` typically occurs because AWS Glue expects the output S3 path to end with a trailing slash to denote a directory. Without it, Glue may interpret the path as a file name rather than a directory, leading to a failure when attempting to write the Parquet files. Adding a trailing slash (e.g., `s3://bucket/output/`) resolves the issue.

Answer

The output format 'parquet' is not supported by Glue

Answer

The input partition path is incorrect because it includes the partition key

Answer

The schema contains a column with a reserved name

Question 13

A company is training a deep learning model on Amazon SageMaker using a dataset stored in Amazon S3. The training job is taking a long time due to I/O bottlenecks. The data is in JSON lines format. Which data preparation step combined with SageMaker's best practices would most effectively reduce training time?

Accepted Answer

Convert the data to RecordIO-Protobuf format and use SageMaker's Pipe mode for training.. Option C is correct because converting JSON lines data to RecordIO-Protobuf format allows SageMaker's Pipe mode to stream data directly from Amazon S3 to the training algorithm without writing to disk, eliminating I/O bottlenecks. Pipe mode uses a FIFO pipe (named pipe) to feed data sequentially, which significantly reduces training time for deep learning models that iterate over the dataset multiple times.

Answer

Convert the JSON lines files to CSV format and use SageMaker's File mode for training.

Answer

Compress the JSON lines files using gzip and use File mode with local caching.

Answer

Split the data into multiple smaller files and use multiple training instances to parallelize.

Question 14

A data scientist needs to prepare a dataset for a binary classification model. The dataset contains 100,000 records with 50 features, including categorical variables with high cardinality, missing values in 30% of records for a key numeric feature, and a severe class imbalance (5% positive class). The data is stored in an Amazon S3 bucket. Which TWO actions should the data scientist take to improve model performance and ensure robust data preparation? (Choose two.)

Accepted Answer

Use stratified sampling to split the dataset into training and test sets, preserving the class imbalance ratio.. Option B is correct because standard scaling is important for distance-based models, and option D is correct because stratified sampling preserves class distribution in train/test split. Option A is wrong because deleting records with missing values would discard 30% of data, leading to loss of information and potential bias. Option C is wrong because one-hot encoding high-cardinality features creates too many dummy variables, causing the curse of dimensionality. Option E is wrong because random undersampling can discard valuable majority class examples, reducing model performance.

Answer

Delete all records with missing values to ensure data integrity.

Answer

Apply one-hot encoding to all categorical features regardless of cardinality.

Answer

Randomly undersample the majority class to balance the dataset before training.

Question 15

A team is using SageMaker Pipelines to automate retraining and deployment. They want to trigger the pipeline automatically when new training data is available in an S3 bucket. Which approach should they use?

Accepted Answer

Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events. Option A is correct because Amazon EventBridge can directly capture S3 PutObject events and invoke a SageMaker Pipeline execution as a target. This provides a fully event-driven, serverless integration without polling or manual intervention, aligning with best practices for automating ML workflows when new data arrives.

Answer

Register the pipeline as a model package in SageMaker Model Registry

Answer

Configure a cron job to run the pipeline every hour

Answer

Use AWS Step Functions to poll the S3 bucket and start the pipeline when a new object appears

Question 16

During a blue/green deployment of a SageMaker endpoint, the team notices that traffic is not being fully shifted to the new variant after the update. The endpoint has two variants with equal initial weights (50% each). The team wants to shift 100% traffic to the new variant. What is the most likely cause?

Accepted Answer

The new variant's model container is failing health checks, so traffic is not routed to it. Option B is correct because SageMaker endpoints route traffic only to variants that pass health checks. If the new variant's model container fails health checks (e.g., due to a misconfigured inference script or incompatible dependencies), SageMaker will not send any traffic to it, regardless of the weight setting. This explains why traffic remains stuck at 50% on the old variant despite the intended shift to 100%.

Answer

The new variant is using a different instance type that is not supported in the same endpoint

Answer

The new variant's weight was set to 100 but the maximum weight per variant is 50

Answer

The endpoint's load balancer is misconfigured and not forwarding traffic to the new variant

Question 17

A data scientist wants to automate retraining of a model weekly and deploy the new model automatically after passing validation. Which AWS service combination is best?

Accepted Answer

SageMaker Pipelines + AWS Step Functions. SageMaker Pipelines orchestrates the ML workflow including training and validation, and Step Functions can trigger deployment. SageMaker alone lacks native scheduling, and Lambda cannot orchestrate complex workflows.

Answer

Amazon EventBridge + SageMaker training job

Answer

Amazon SageMaker Autopilot

Answer

AWS Lambda + SageMaker training job

Question 18

A team notices that inference requests to their SageMaker endpoint are failing with '504 Gateway Timeout' for large payloads. What change should be made?

Accepted Answer

Increase the endpoint's invocation timeout. A 504 Gateway Timeout indicates that the SageMaker endpoint's invocation timeout (default 60 seconds) was exceeded while processing a large payload. Increasing the invocation timeout allows the endpoint more time to complete inference for large payloads, resolving the timeout error.

Answer

Enable data capture on the endpoint

Answer

Deploy a shadow endpoint for testing

Answer

Switch to a multi-model endpoint

Question 19

A team uses SageMaker Pipelines for CI/CD. The training step fails due to insufficient memory. How to fix without rewriting code?

Accepted Answer

Increase the instance type in the pipeline step configuration. Option C is correct because SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.

Answer

Modify the training algorithm to use less memory

Answer

Reduce the batch size in the training script

Answer

Enable managed spot training

Question 20

An ML team is deploying a model using SageMaker. The model requires GPU inference and must be available in multiple AWS regions for low latency. The team has created a multi-model endpoint with GPU instances. After deployment, they notice high latency spikes when a new model is loaded. What is the most likely cause?

Accepted Answer

The team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.. A multi-model endpoint (MME) loads models on demand from Amazon S3 into the instance's memory. When a new model is requested and not already cached, SageMaker must download the model artifacts and load them into GPU memory, which is a time-consuming operation that causes a latency spike for the first inference request. This cold-start behavior is inherent to MMEs and explains the observed spikes.

Answer

The endpoint is configured with a single production variant, causing all traffic to overload one instance.

Answer

The endpoint is using the wrong instance type that lacks sufficient GPU memory.

Answer

The model is too large for the specified container memory, causing swap to disk.