MLA-C01 Practice Test 1 — 25 Questions

Question 1

A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to access an S3 bucket that contains sensitive data. The team wants to follow the principle of least privilege. How should the team grant access?

Accepted Answer

Create an IAM role with S3 access and assign it as the SageMaker execution role for the training job.. Option A is correct because SageMaker training jobs use an IAM execution role to grant permissions to AWS services like S3. By creating a dedicated IAM role with only the necessary S3 actions (e.g., s3:GetObject, s3:PutObject) and assigning it as the SageMaker execution role, the team follows the principle of least privilege. SageMaker automatically assumes this role via AWS Security Token Service (STS) to access the S3 bucket on behalf of the container, without embedding credentials.

Answer

Attach an IAM instance profile to the training instance with permissions to the bucket.

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Configure an S3 bucket policy that grants access to the training job's ARN.

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Store AWS access keys in the container image and use them to access the bucket.

Question 2

A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most likely cause?

Accepted Answer

The inference code in the Docker container outputs a different response format than expected by the endpoint.. The error in CloudWatch Logs indicates that the SageMaker endpoint received a response from the inference container that does not match the expected format. SageMaker endpoints require the container to output a response body in a specific format (e.g., JSON or CSV) as defined by the model's Accept header or the endpoint's serialization configuration. If the inference code returns a malformed or unexpected response (e.g., raw bytes, incorrect JSON structure, or missing fields), the endpoint will fail to parse it and log an error. This is the most direct cause of the observed error.

Answer

The model file is corrupted during deployment.

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The data capture configuration is incorrectly set to capture only the response body.

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The endpoint is overloaded and dropping requests.

Question 3

A company wants to reduce costs for a SageMaker real-time endpoint that receives predictable traffic patterns: high during business hours and low at night. The model is a small PyTorch model. Which cost-saving strategy is most suitable?

Accepted Answer

Configure auto-scaling with a scheduled scaling policy to add instances during business hours and reduce at night. Auto-scaling with a schedule can adjust instance count based on time, matching capacity to demand. This is more efficient than manual scaling or using a larger instance.

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Use a single large instance to handle peak load

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Use a multi-model endpoint with multiple models

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Switch to batch transform jobs and run nightly

Question 4

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

Accepted Answer

Enable data capture on the endpoint. Option B is correct because enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.

Answer

Deploy a shadow endpoint for comparison

Answer

Use SageMaker Debugger for monitoring

Question 5

An ML team uses AWS Step Functions to orchestrate a retraining pipeline triggered by EventBridge when new training data arrives. The pipeline includes a SageMaker training job and a model evaluation. If evaluation fails, the team wants to send an alert. How should they implement this?

Accepted Answer

Add a Catch rule in the Step Functions state machine to invoke a Lambda alert function. Step Functions supports error handling via Catch rules; a Catch on the training or evaluation task can transition to a Lambda function that sends an alert.

Answer

Use SQS dead-letter queue for failed training jobs

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Configure SageMaker training job to publish to SNS on failure

Answer

Use EventBridge to monitor the training job status

Question 6

A financial services company needs to enforce that only approved model versions are deployed to production. They use SageMaker Model Registry to track versions, with an approval workflow. Which action must they take in the model registry to ensure only approved models can be deployed?

Accepted Answer

Set the model version status to 'Approved' in the Model Registry. Option A is correct because the SageMaker Model Registry uses a status field to control the lifecycle of model versions. By setting the model version status to 'Approved', the company can enforce that only approved models are deployable, as SageMaker's deployment APIs (e.g., CreateModel, CreateEndpointConfig) can be configured to require an 'Approved' status. This integrates with the approval workflow, ensuring that unapproved or pending versions are blocked from production deployment.

Answer

Tag the model version as 'production-ready'

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Manually move the model artifact to a production S3 bucket

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Use AWS IAM policies to restrict deployment to specific model ARNs

Question 7

A machine learning engineer needs to deploy a TensorFlow model that requires a custom inference environment with specific system libraries. The model will be used in a real-time application with variable traffic. They want to minimize cold start latency. Which SageMaker hosting option should they choose?

Accepted Answer

SageMaker real-time endpoint with a custom container. SageMaker real-time endpoints with a custom container are the correct choice because they provide persistent, always-on infrastructure that eliminates cold start latency. By packaging the TensorFlow model with required system libraries in a custom Docker image, the engineer ensures the inference environment is ready immediately, and the endpoint can scale to handle variable traffic with minimal delay.

Answer

SageMaker Serverless Inference with a custom container

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SageMaker Multi-Model Endpoint with a custom container

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SageMaker Asynchronous Inference with a custom container

Question 8

A team uses SageMaker Pipelines to retrain a model nightly. They want to skip the training step if the new data is unchanged (same checksum as previous run) to save cost and time. Which pipeline configuration achieves this?

Accepted Answer

Use a ConditionStep that compares the current data checksum to the previous run's checksum, and branch to a NoOp step if unchanged. Option C is correct because SageMaker Pipelines' ConditionStep allows you to evaluate a condition—such as comparing the current data checksum to a stored previous checksum—and branch accordingly. If the checksums match, you can route to a NoOp step (which does nothing) instead of executing the training step, thereby skipping the training and saving cost and time. This is the native, recommended pattern for conditional execution in SageMaker Pipelines.

Answer

Enable pipeline caching on the training step

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Use a Lambda step to check data before running the training step

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Set the training step's CacheConfig with a TTL of 24 hours

Question 9

A data science team uses Amazon SageMaker Model Monitor to detect data drift in production. They notice that the schema of incoming data (number of features) has changed compared to the training baseline. Which type of monitor is BEST suited to detect this issue?

Accepted Answer

Data quality monitor. The Data quality monitor in SageMaker Model Monitor is specifically designed to detect violations in the input data schema, such as changes in the number of features, feature types, or missing values, by comparing incoming data against a baseline computed from the training dataset. Since the issue is a structural change in the schema (number of features), the Data quality monitor is the correct choice.

Answer

Bias drift monitor

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Feature attribution drift monitor

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Model quality monitor

Question 10

A company deploys a model for credit risk assessment on a SageMaker endpoint. To comply with internal policies, they must ensure that the endpoint only allows inference requests from within a specific VPC and that the data is encrypted at rest. Which configuration meets these requirements?

Accepted Answer

Configure endpoint in VPC-only mode and enable KMS encryption for the endpoint. Option D is correct because configuring the SageMaker endpoint in VPC-only mode ensures that inference requests can only originate from within the specified VPC, satisfying the network restriction requirement. Enabling KMS encryption for the endpoint encrypts the model data and inference data at rest using a customer-managed key, meeting the encryption-at-rest policy.

Answer

Enable network isolation mode and use KMS for endpoint encryption

Answer

Configure endpoint in VPC-only mode and enable inter-container traffic encryption

Answer

Deploy endpoint in a public subnet and use a security group to restrict traffic

Question 11

A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accuracy is 0.99 but validation accuracy is 0.75. Which action is MOST likely to reduce overfitting?

Accepted Answer

Increase the regularization strength. The model shows high training accuracy (0.99) but significantly lower validation accuracy (0.75), which is a classic sign of overfitting. Increasing the regularization strength (e.g., L1 or L2 penalty) in logistic regression directly penalizes large coefficients, reducing the model's complexity and improving generalization. This is the most direct way to address overfitting in a logistic regression model.

Answer

Increase the number of features

Answer

Use a more complex model like XGBoost

Answer

Use stratified cross-validation

Question 12

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

Accepted Answer

Store training data in Amazon S3 in a shuffled and compressed format. Storing training data in Amazon S3 in a shuffled and compressed format (Option A) optimizes training performance because shuffling prevents biased gradient updates during stochastic gradient descent, while compression reduces I/O overhead and network transfer time. SageMaker's Pipe mode can then stream this compressed data directly to the training algorithm without intermediate disk writes, further accelerating throughput.

Answer

Use the largest instance type available

Answer

Increase the number of layers in the model to improve accuracy

Question 13

A company is using SageMaker to train a neural network for image classification. The training job is taking too long. The team wants to reduce training time without sacrificing model accuracy. Which approach should they recommend?

Accepted Answer

Use a GPU-based instance such as ml.p3.2xlarge. Option B is correct because GPU-based instances like ml.p3.2xlarge are specifically designed for parallel processing of matrix operations, which are fundamental to neural network training. By offloading compute-intensive tensor operations to GPU cores, training time can be significantly reduced without altering the model architecture or data, thus preserving accuracy.

Answer

Increase the batch size to the maximum possible

Answer

Use a learning rate scheduler that reduces the learning rate over time

Answer

Add more convolutional layers to the model

Question 14

A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target variable is binary with 15% churn rate. The team wants to use logistic regression. Which data preprocessing step is MOST important to ensure proper convergence?

Accepted Answer

Standardize the features to have zero mean and unit variance. Logistic regression uses gradient descent or similar optimization algorithms that rely on the scale of the features. When features have different units or magnitudes, the cost function becomes elongated, causing slow or unstable convergence. Standardizing to zero mean and unit variance ensures that all features contribute equally to the gradient updates, leading to faster and more reliable convergence.

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Remove correlated features to reduce multicollinearity

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Impute missing values with the median

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Apply SMOTE to balance the classes

Question 15

A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multiple time zones. The engineer needs to create a feature for hour-of-day consistent across all records. Which approach ensures correctness?

Accepted Answer

Convert all timestamps to UTC during the ETL process, then extract hour. Option D is correct because converting all timestamps to UTC during the ETL process ensures a consistent time zone reference before extracting the hour-of-day feature. This avoids ambiguity from mixed time zones and aligns with best practices for machine learning feature engineering. AWS Glue ETL with Apache Spark provides built-in functions like `to_utc_timestamp()` to perform this conversion reliably.

Answer

Convert all timestamps to UTC in the ETL script using Spark's from_utc_timestamp

Answer

Use AWS Glue's built-in transform to parse timestamps with timezone offsets

Answer

Use Python's datetime.strptime with tzlocal

Question 16

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 17

A data scientist is preparing a dataset for a binary classification model to predict customer churn. The dataset contains a timestamp column 'signup_date' that is not relevant for the prediction. What is the most appropriate action to handle this column?

Accepted Answer

Drop the 'signup_date' column from the dataset.. Option D is correct because the 'signup_date' column is explicitly stated as not relevant for the prediction. In binary classification for customer churn, including an irrelevant timestamp can introduce noise, increase dimensionality, and potentially cause overfitting. Dropping the column is the most appropriate action to maintain model simplicity and focus on predictive features.

Answer

Apply one-hot encoding to the year, month, and day components.

Answer

Convert the timestamp to a numeric feature (e.g., days since signup) and include it.

Answer

Use leave-one-out encoding based on the target variable.

Question 18

An ML team wants to deploy a model that was trained using XGBoost in SageMaker. They want to use the built-in XGBoost algorithm container for inference. Which inference option requires the least custom code?

Accepted Answer

Deploy to a real-time endpoint using the built-in XGBoost container. Option B is correct because the built-in XGBoost container in SageMaker is pre-configured with the XGBoost serving stack, including the necessary inference code and dependencies. Deploying a model trained with XGBoost to a real-time endpoint using this container requires no custom inference script or Docker image, only the model artifact and endpoint configuration. This minimizes custom code to just the SageMaker SDK calls for creating the model and endpoint.

Answer

Create a custom Docker container with XGBoost and deploy to an endpoint

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Attach Elastic Inference to a generic container

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Use SageMaker Python SDK to download the model and run local inference

Question 19

An ML engineer runs the CLI command shown in the exhibit. However, the training job fails immediately with an error: 'Unable to assume role'. What is the most likely cause?

Accepted Answer

The IAM role's trust policy does not grant SageMaker permission to assume the role.. The 'Unable to assume role' error indicates that SageMaker cannot assume the IAM role specified in the CLI command. This is a trust policy issue: the role's trust policy must include SageMaker as a trusted service (i.e., `"Service": "sagemaker.amazonaws.com"`). Without this, SageMaker is not authorized to assume the role, regardless of the role's permissions.

Answer

The IAM role 'SageMakerExecutionRole' does not have permission to create the training job.

Answer

The training image in ECR does not exist.

Answer

The S3 bucket 'my-bucket' does not exist.

Question 20

Refer to the exhibit. A data scientist creates a SageMaker Pipeline definition using the JSON shown. The pipeline runs successfully, but the scientist notices that the training step did not use the parameter 'TrainingInstanceCount' defined in Parameters. Why did this happen?

Accepted Answer

The steps do not reference the Parameters; the values are hardcoded in the step definitions.. Option C is correct because the SageMaker Pipeline definition shows that the training step's `InstanceCount` field is hardcoded to `1` in the step definition, rather than referencing the `TrainingInstanceCount` parameter using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). In SageMaker Pipelines, parameters defined in the `Parameters` section must be explicitly referenced within the step definitions using the `Parameters` object; otherwise, the pipeline uses the hardcoded values and ignores the parameters entirely.

Answer

The pipeline encountered a runtime error and fell back to default values.

Answer

The parameter name has a typo; it should be 'TrainingInstanceCount' not 'TrainingInstanceCount'.

Answer

The training image is not compatible with the specified instance type.