MLA-C01 Practice Exam 56 — 20 Questions

Question 1

A company uses SageMaker Model Monitor for feature attribution drift monitoring with SHAP. Which THREE prerequisites must be in place before starting the monitoring schedule? (Select THREE)

Accepted Answer

A SageMaker Clarify processing job that computes SHAP values on the captured data. SageMaker Model Monitor requires a Clarify processing job to compute SHAP values on the captured data as part of the feature attribution drift monitoring setup. This job generates the necessary SHAP explainability values that are compared against the baseline to detect drift.

Answer

Ground truth labels for the inference data

Answer

Baseline constraints file for data quality

Question 2

During data preparation for a regression model, a data scientist notices that two features have a Pearson correlation coefficient of 0.95. The scientist is concerned about multicollinearity. Which action should be taken to address this issue?

Accepted Answer

Remove one of the two features. Removing one of the highly correlated features reduces multicollinearity without losing much information, as they are nearly linearly dependent. Standardization does not fix multicollinearity. PCA would reduce dimensionality but may harm interpretability. Keeping both can destabilize coefficient estimates.

Answer

Apply PCA to reduce dimensionality to a single component

Answer

Keep both features as they are because linear models are robust to multicollinearity

Answer

Standardize both features using StandardScaler

Question 3

A data scientist is training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

Accepted Answer

Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.. The scale_pos_weight parameter in XGBoost directly adjusts the loss function to penalize misclassifications of the minority class more heavily, effectively handling class imbalance without modifying the dataset. This avoids data leakage because the weighting is applied during training only, not during preprocessing, and does not involve any synthetic data generation or resampling that could inadvertently expose test information.

Answer

Undersample the majority class to create a balanced dataset, then split.

Answer

Oversample the minority class using SMOTE on the entire dataset before splitting into train/validation sets.

Answer

Randomly oversample the minority class by duplicating rows, then perform stratified train/test split.

Question 4

A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?

Accepted Answer

Use SageMaker Batch Transform with separate jobs for each model.. SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.

Answer

Use SageMaker Pipelines to run inference as part of the pipeline.

Answer

Create a single SageMaker endpoint for all models and update the model periodically.

Answer

Deploy each model to a separate SageMaker endpoint and delete after use.

Question 5

A financial services company must ensure that all data used by Amazon SageMaker training jobs is encrypted at rest. The company wants to use a customer-managed key (CMK) for the encryption. Which steps are necessary to achieve this? (Choose TWO.)

Accepted Answer

Create a CMK in AWS KMS and add the SageMaker service principal to the key policy to allow it to use the key.. To use a customer-managed key (CMK) for encrypting SageMaker training job data, you must first create a CMK in AWS KMS and then add the SageMaker service principal (sagemaker.amazonaws.com) to the key policy. This grants SageMaker the necessary permissions to use the key for encrypting the ML storage volume (e.g., EBS volumes) attached to the training instances. Without this policy statement, SageMaker cannot access the CMK, and the encryption request will fail.

Answer

Enable SageMaker's default encryption for the training job by setting the EnableDefaultEncryption flag.

Answer

Enable S3 default encryption using the CMK on all buckets containing training data.

Answer

Use CloudWatch Logs encryption to protect the training logs.

Question 6

A machine learning team is training a large natural language processing model on Amazon SageMaker using the SageMaker Hugging Face container. The training job runs on multiple instances and uses Managed Spot Training to reduce costs. However, the job frequently gets interrupted by Spot interruptions, causing long training times. What should the team do to mitigate this issue?

Accepted Answer

Enable checkpointing and increase the number of save intervals. Enabling checkpointing and saving intermediate model states at appropriate intervals allows the training job to resume from the last checkpoint after a Spot interruption, significantly reducing wasted time. Increasing save intervals means more frequent saving, which reduces work lost. Reserved capacity does not help with interruptions; using larger instances doesn't prevent interruptions; disabling Spot increases cost.

Answer

Use a reserved capacity with Savings Plans

Answer

Use a larger instance type to finish faster

Answer

Disable Managed Spot Training and use On-Demand instances

Question 7

A large enterprise has multiple SageMaker endpoints serving models for different business units. Each endpoint uses a separate instance type and scaling policy. The enterprise wants to implement a unified monitoring and logging solution to track endpoint health, latency, and errors across all endpoints. They also want to set up alerts when the error rate exceeds 5% over a 5-minute period. The solution must be centralized and use AWS-native services. Which solution should the team implement?

Accepted Answer

Use Amazon CloudWatch dashboards to aggregate metrics from all endpoints, and create a composite alarm based on the Sum of 5xx error counts across endpoints.. Amazon CloudWatch can natively ingest SageMaker endpoint metrics (e.g., 5xx error counts, latency, invocation counts) without additional configuration. By creating a CloudWatch dashboard, you aggregate metrics from all endpoints into a single view, and a composite alarm using the Sum statistic across endpoints over a 5-minute period directly triggers when the error rate exceeds 5%. This approach is fully centralized, uses only AWS-native services, and requires no custom code or data streaming.

Answer

Enable SageMaker Model Monitor data capture on each endpoint and stream captured data to Amazon Kinesis for analysis.

Answer

Use AWS CloudTrail to audit all API calls to SageMaker and set up alarms on error responses.

Answer

Use Amazon CloudWatch Logs to collect logs from each endpoint, and use a Lambda function to parse logs and calculate error rates, then publish custom metrics.

Question 8

A data scientist wants to version control trained models and manage approvals for deployment. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry.. SageMaker Model Registry is the correct feature because it is specifically designed for versioning trained models, tracking their metadata (e.g., training job, metrics), and managing approval workflows for deployment stages (e.g., Pending, Approved, Rejected). This directly addresses the data scientist's need to version control models and manage deployment approvals.

Answer

SageMaker Experiments.

Answer

SageMaker Feature Store.

Answer

SageMaker Ground Truth.

Question 9

A team wants to automate the retraining and deployment of an ML model whenever new labeled data arrives in S3. The workflow includes data preprocessing, training, evaluation, and conditional deployment. Which AWS service is best suited for orchestrating this end-to-end pipeline?

Accepted Answer

Amazon SageMaker Pipelines triggered by S3 events via EventBridge.. Amazon SageMaker Pipelines is purpose-built for ML workflows, offering native integration with SageMaker for training, evaluation, and conditional deployment steps. Triggered by S3 events via Amazon EventBridge, it automates the end-to-end pipeline from data preprocessing to conditional model deployment without requiring custom orchestration code.

Answer

AWS Step Functions with Lambda functions for each step.

Answer

AWS Glue workflows with triggers based on S3 events.

Answer

AWS CodePipeline with source from S3 and build from CodeBuild.

Question 10

A machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?

Accepted Answer

Apply target encoding (mean encoding) to the high-cardinality features.. For high-cardinality categorical features, target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which captures information without creating a large number of dummy variables. One-hot encoding would create too many features. Label encoding implies ordinal relationships. Hash encoding can cause collisions.

Answer

Apply hash encoding to map categories to a fixed number of buckets.

Answer

Apply one-hot encoding to all categorical features.

Answer

Apply label encoding to assign integer values to each category.

Question 11

A company has a batch transform job in Amazon SageMaker that processes large datasets every night. Recently, the job has been failing sporadically with an out-of-memory error. The data size has not increased. What is the MOST likely cause?

Accepted Answer

The custom inference code has a memory leak that gradually consumes available memory.. A memory leak in custom inference code would cause gradual memory consumption over time, eventually leading to out-of-memory errors even if the dataset size remains unchanged. SageMaker batch transform jobs run inference on each record independently, so a leak that accumulates across records (e.g., not releasing tensors or file handles) would cause sporadic failures as the job processes more data.

Answer

The data distribution has shifted, causing different memory usage patterns.

Answer

The instance type is not large enough to handle the dataset.

Answer

The batch transform input data has increased in size.

Question 12

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. 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 13

A company is deploying a SageMaker real-time endpoint and needs to monitor inference latency. Which THREE metrics are available from SageMaker for this purpose? (Choose THREE.)

Accepted Answer

OverheadLatency. OverheadLatency is a SageMaker metric that measures the time taken by the SageMaker infrastructure to process the request before and after invoking the model, including network overhead and framework overhead. It is one of the three metrics specifically designed to monitor inference latency for real-time endpoints.

Answer

Invocations

Answer

MemoryUtilization

Question 14

An MLOps engineer is setting up a SageMaker endpoint for a model that performs inference on large images. The model is containerized and expects input in a specific format. The team wants to preprocess the images (resize and normalize) before passing them to the model. What is the most efficient way to implement this?

Accepted Answer

Configure SageMaker to use a preprocessing container as the first step of an inference pipeline, followed by the model container.. SageMaker Inference Pipelines allow you to chain multiple containers in a serial fashion, where the output of one container becomes the input of the next. By placing a preprocessing container as the first step, you can resize and normalize large images before passing them to the model container, which keeps the model container focused on inference and avoids unnecessary data transfer or custom code. This is the most efficient and natively supported approach within SageMaker for multi-step inference workflows.

Answer

Use Amazon API Gateway to perform request transformation before forwarding to the endpoint.

Answer

Package the preprocessing logic into the same Docker container as the model.

Answer

Use a Lambda function as a proxy to preprocess requests before calling the SageMaker endpoint.

Question 15

A company uses Amazon SageMaker Ground Truth to create a labeled dataset. They want to monitor the accuracy of human labelers during the labeling process. Which metric should they track?

Accepted Answer

Accuracy against blinded ground truth. Tracking accuracy against blinded ground truth (known as audit tasks) is the standard way to measure labeler performance. Options A and B are operational metrics not directly measuring accuracy. Option D is not directly accuracy.

Answer

Labeling job cost

Answer

Number of tasks completed

Answer

Task acceptance rate

Question 16

A company wants to ensure that only authorized users and services can invoke a SageMaker real-time endpoint. Which AWS service can be used to manage access control?

Accepted Answer

AWS Identity and Access Management (IAM). AWS Identity and Access Management (IAM) is the correct service because it allows you to create fine-grained permissions policies that control which users, roles, or services can invoke a SageMaker real-time endpoint via the InvokeEndpoint API. By attaching IAM policies to principals (e.g., IAM users, roles, or federated identities), you can restrict invocation based on conditions such as source IP, VPC endpoint, or MFA, ensuring only authorized entities can send inference requests.

Answer

Amazon CloudWatch

Answer

AWS CloudTrail

Answer

AWS Config

Question 17

A data scientist is using Amazon SageMaker Data Wrangler for data preparation. Which two tasks can be performed using Data Wrangler's built-in transforms? (Choose two.)

Accepted Answer

Encoding categorical variables. Amazon SageMaker Data Wrangler includes a built-in transform for encoding categorical variables, such as one-hot encoding or ordinal encoding, directly within its visual interface. This allows data scientists to convert categorical data into numerical formats without writing custom code, streamlining the data preparation pipeline.

Answer

Running a SQL query on the data

Answer

Creating an ensemble of models

Answer

Training a custom machine learning model

Question 18

A company uses SageMaker to orchestrate a training pipeline with multiple steps including preprocessing, training, and evaluation. They want to ensure that each step can be reused and tracked. Which three SageMaker features support this? (Select THREE.)

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines is correct because it provides a directed acyclic graph (DAG) of steps that can be defined, parameterized, and reused across different runs. Each step (preprocessing, training, evaluation) is a distinct, versioned component that can be independently tracked and re-executed, enabling modular orchestration of ML workflows.

Answer

SageMaker Clarify

Answer

SageMaker Model Monitor

Question 19

A company wants to track the lineage of their ML models for reproducibility and auditability. Which THREE services or features should they use together to achieve this? (Choose THREE.)

Accepted Answer

Amazon S3 versioning. Amazon S3 versioning is correct because it preserves every version of an object stored in an S3 bucket, including model artifacts, datasets, and configuration files. By enabling versioning, you can retrieve and revert to any previous version of a model artifact, which is essential for reproducibility and auditability. This directly supports tracking the lineage of ML models by ensuring that the exact input data and model binaries used in a specific experiment are never overwritten or lost.

Answer

AWS CloudTrail

Answer

AWS Config

Question 20

A machine learning team needs to automatically retrain a model when concept drift is detected in the deployed endpoint's predictions. Which TWO steps should they take? (Choose TWO.)

Accepted Answer

Create a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job. Model Quality Monitor compares predictions with ground truth to detect concept drift. When an alarm triggers, a Lambda function can start a retraining pipeline. Data Quality Monitor is for data drift, not concept drift.

Answer

Schedule retraining with Amazon EventBridge on a fixed schedule

Answer

Configure SageMaker Model Monitor - Data Quality Monitor to detect input drift

Answer

Use SageMaker Clarify to monitor bias drift