MLA-C01 Practice Test 49 — 15 Questions

Question 1

A company has deployed a model to a SageMaker endpoint. The security team wants to ensure that all traffic between the endpoint and the client application is encrypted and that the endpoint is not accessible from the internet. Which TWO actions should the company take? (Choose TWO.)

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

Accepted Answer

Configure the SageMaker endpoint to be VPC-only by setting the endpoint's VPC configuration.. Configuring a SageMaker endpoint as VPC-only ensures that the endpoint is not publicly accessible; it can only be reached from within the specified VPC, satisfying the security team's requirement to block internet access. Option D is correct because enabling HTTPS on the endpoint using a custom certificate from AWS Certificate Manager (ACM) encrypts all data in transit between the client and the endpoint, meeting the encryption requirement.

Answer

Place the endpoint behind an API Gateway and call it from the client.

Answer

Create the endpoint with a public endpoint and allow only the client's IP address via security group.

Answer

Use AWS KMS to encrypt data in transit between the client and the endpoint.

Question 2

A data scientist is using SageMaker built-in XGBoost algorithm for a regression problem. Which metric is most appropriate as the objective metric for hyperparameter tuning?

Accepted Answer

RMSE. For regression tasks, RMSE is a common objective metric. AUC is for classification, F1 is for classification, and NDCG is for ranking.

Answer

NDCG

Answer

AUC

Answer

F1

Question 3

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

Accepted Answer

Temporal split based on date. A temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.

Answer

Stratified split based on target

Answer

Random split with 70/20/10

Answer

K-fold cross-validation

Question 4

A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?

Accepted Answer

Stratified split. Stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.

Answer

K-fold cross-validation split

Answer

Chronological split

Answer

Random split

Question 5

A machine learning engineer is training a model using SageMaker and wants to set up monitoring to detect if gradients become too large, which could destabilize training. Which SageMaker Debugger built-in rule should they enable?

Accepted Answer

ExplodingGradients. Debugger's built-in rule 'ExplodingGradients' monitors gradient norms and alerts if they exceed a threshold, helping to stabilize training.

Answer

DeadRelu

Answer

LossNotDecreasing

Answer

Overfit

Question 6

A machine learning engineer is setting up automated retraining for a model using SageMaker Pipelines. The pipeline should trigger when a data drift alert is received from Model Monitor. Which event source should the engineer use to initiate the pipeline?

Accepted Answer

Amazon CloudWatch Events (Amazon EventBridge) rule that captures Model Monitor outcome.. Amazon EventBridge (formerly CloudWatch Events) is the native AWS service for reacting to state changes in AWS resources. SageMaker Model Monitor publishes data drift alerts as events to EventBridge, so a rule can be configured to match those specific events and trigger the SageMaker Pipeline execution as a target. This provides a fully managed, event-driven architecture without polling or custom integrations.

Answer

AWS Lambda function that polls CloudWatch logs.

Answer

S3 event notification on the monitoring output bucket.

Answer

SageMaker model monitor webhook.

Question 7

A financial services company needs to deploy a SageMaker endpoint that only accepts inference requests from within a specific VPC and denies all public traffic. The endpoint must also encrypt data in transit between containers. How should the endpoint be configured?

Accepted Answer

Configure the endpoint with network isolation mode and enable inter-container traffic encryption. Enabling network isolation mode ensures the SageMaker endpoint is deployed within a VPC and cannot be accessed from the public internet, satisfying the requirement to deny all public traffic. Additionally, enabling inter-container traffic encryption (using TLS) encrypts data in transit between the containers hosting the model, meeting the encryption requirement. This configuration is specific to SageMaker endpoints and directly addresses both constraints.

Answer

Deploy the endpoint in a public subnet and restrict security group ingress to the VPC CIDR

Answer

Enable VPC-only mode for the endpoint and disable public access

Answer

Use a privateLink endpoint and enable data encryption at rest

Question 8

A data engineer is using AWS Glue to run an ETL job that joins two large datasets and writes the output to S3 for ML training. The job is failing due to out-of-memory errors. Which THREE actions can help resolve this issue? (Select THREE.)

Accepted Answer

Filter unnecessary records early in the transformation. Filtering unnecessary records early in the transformation reduces the amount of data that needs to be processed and shuffled, which directly lowers memory pressure. In AWS Glue, applying filters before joins or aggregations minimizes the dataset size in the Spark execution plan, helping to avoid out-of-memory errors.

Answer

Switch from Spark to Python shell

Answer

Use a smaller worker type

Question 9

A team is training a large language model using PyTorch on SageMaker. They need to reduce training time. The model has 10 billion parameters. Which distributed training strategy should they use?

Accepted Answer

Model parallelism with SageMaker distributed. For large models that do not fit into GPU memory, model parallelism is required. Data parallelism replicates the model on each GPU, which would cause out-of-memory errors.

Answer

Data parallelism with Horovod

Answer

Single GPU training

Answer

Use a larger instance type without parallelism

Question 10

An e-commerce company uses Amazon SageMaker to train a model that predicts click-through rates. The training data includes a timestamp column 'click_time' and a categorical feature 'device_type' (8 values). They notice that the model's performance degrades over time because the data distribution shifts. They want to ensure the training data represents the most recent behavior. The data is stored in a daily partitioned S3 bucket (e.g., s3://bucket/data/2024-01-01/). The total dataset size is 500 GB. Which approach should they take to prepare the training data while minimizing bias and cost?

Accepted Answer

Select only the data from the last 30 days to train the model.. Selecting only the last 30 days of data directly addresses the data distribution shift by focusing on the most recent user behavior, which is critical for click-through rate prediction. This approach minimizes bias from outdated patterns and reduces training cost by using a smaller, relevant dataset (approximately 500 GB / 365 * 30 ≈ 41 GB). SageMaker training jobs benefit from this reduced volume through faster data loading and lower compute costs.

Answer

Take a random sample of 10% of the rows from the entire dataset.

Answer

Use all historical data and let the model learn the temporal patterns.

Answer

Downsample older data exponentially so that recent data is overrepresented.

Question 11

An ML team is using SageMaker Model Registry to manage model versions. After training a new model version, they register it with an 'Approved' status. The CI/CD pipeline automatically deploys the latest approved model to a staging endpoint. However, the pipeline fails with an error: 'Cannot deploy model because the model version is not approved.' The model version is clearly approved in the registry. What is the most likely cause?

Accepted Answer

The approval status was set on the model package group, not on the specific model version.. In SageMaker Model Registry, approval is a property of a specific model version within a model package group, not of the model package group itself. The error indicates the pipeline is likely referencing the model package group ARN or a version that lacks explicit approval, even though the team believes the model is approved. The CI/CD pipeline must use the exact model version ARN that has the 'Approved' status to deploy successfully.

Answer

The pipeline is using the model package ARN instead of the model version ARN.

Answer

The model version is approved but the pipeline uses a different version that is still pending.

Answer

The SageMaker endpoint configuration does not have the necessary IAM permissions to read the registry.

Question 12

An ML engineer is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The team needs to retrieve point-in-time feature values for training, ensuring no future data leaks. Which THREE configurations are required? (Choose 3)

Accepted Answer

Enable the offline store for the feature group. Point-in-time queries require recording timestamps, using the offline store (which supports historical queries), and enabling the feature group with the correct configuration. The online store is for real-time, not historical. As-of timestamp is a parameter for queries but not a configuration. Record identifier is for record keys.

Answer

Use the GetRecord API with an as-of timestamp

Answer

Enable the online store for the feature group

Question 13

A machine learning engineer is using SageMaker Processing to run a scikit-learn preprocessing script. The script reads a CSV file from S3, applies a StandardScaler, and writes the output. The job fails with a 'MemoryError'. Which change should the engineer make to the data preparation process?

Accepted Answer

Increase the instance memory size for the processing job. The MemoryError indicates that the processing job's instance does not have enough RAM to hold the dataset and the intermediate results of the StandardScaler (which computes mean and variance in memory). Increasing the instance memory size (Option B) directly resolves this by providing more RAM for the scikit-learn operations. SageMaker Processing jobs allow you to choose instances with larger memory, such as the r5 or r6i families, to accommodate larger datasets.

Answer

Use a SageMaker Spark container instead of scikit-learn

Answer

Write the output as Parquet instead of CSV

Answer

Standardize the features before loading into the DataFrame

Question 14

A machine learning engineer is preparing a dataset for a multiclass classification task. The dataset has 10 features and 100,000 rows. Which TWO techniques should the engineer use to reduce the risk of overfitting during data preparation?

Accepted Answer

Data augmentation (e.g., adding noise). Data augmentation (A) is correct because it artificially increases the diversity of the training set by adding noise or transformations, which helps the model generalize better and reduces overfitting. Feature selection using correlation analysis (E) is correct because it removes redundant or highly correlated features, simplifying the model and minimizing the risk of learning noise from irrelevant predictors.

Answer

SMOTE to balance classes

Answer

One-hot encoding of all categorical features

Answer

Log transformation of skewed features

Question 15

A data engineer is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column 'review_date' with timestamps. The engineer wants to extract the day of the week as a new feature. How should this transformation be performed in Data Wrangler?

Accepted Answer

Use the 'day_of_week' transform on the 'review_date' column. Amazon SageMaker Data Wrangler includes a built-in 'day_of_week' transform that directly extracts the day of the week (e.g., Monday, Tuesday) from a timestamp column without requiring custom code or additional formatting. This transform is optimized for Data Wrangler's visual interface and integrates seamlessly with its processing pipeline.

Answer

Write a custom Python script using pandas dt.day_name()

Answer

Use one-hot encoding on the timestamp

Answer

Use the 'extract' transform with format '%A'