MLA-C01 Practice Exam 54 — 20 Questions

Question 1

A machine learning team is using SageMaker Studio for model development. They need to restrict all internet access from Studio notebooks and ensure that all data stays within a VPC. Which configuration should they use?

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

Accepted Answer

Configure the Studio domain with VPC-only mode and disable direct internet access. Configuring a SageMaker Studio domain with VPC-only mode and disabling direct internet access ensures that all Studio notebook traffic stays within the VPC and has no route to the public internet. This is the only configuration that both restricts internet access and enforces data residency within the VPC by using VPC-only networking, which does not rely on an internet gateway or NAT gateway for outbound traffic.

Answer

Use a private link to connect Studio to the VPC

Answer

Enable network isolation mode for the Studio domain

Answer

Attach a security group that blocks all outbound traffic

Question 2

Refer to the exhibit. A SageMaker execution role has the IAM policy shown. The team attempts to run a training job that writes results to 's3://my-bucket/training/output/model.tar.gz'. What will happen?

Accepted Answer

The training job will succeed and write the model artifact.. The training job will succeed because the Allow statement in the IAM policy explicitly grants s3:PutObject on the specific object 's3://my-bucket/training/output/model.tar.gz', and the Deny statement only blocks PutObject on objects with a 'training/' prefix in the key. Since the target object key is 'training/output/model.tar.gz', it does not start with 'training/' (the prefix is 'training/output/'), so the Deny does not apply. The Allow is therefore effective, and the model artifact is written successfully.

Answer

The training job will fail because the Deny statement blocks all PutObject actions.

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The training job will fail because the Deny statement overrides the Allow.

Answer

The training job will succeed, but the output file will be encrypted with a different key.

Question 3

A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a random forest model. The engineer notices that the tuning job is taking too long and many hyperparameter combinations are being evaluated but not improving the objective metric. Which action should the engineer take to make the tuning more efficient?

Accepted Answer

Enable early stopping for the tuning job. Enabling early stopping in SageMaker Automatic Model Tuning (AMT) terminates poorly performing training jobs before they complete, which reduces wasted compute time and speeds up the tuning process. This is especially effective when using Bayesian optimization, as it allows the algorithm to focus on promising hyperparameter regions and avoid evaluating combinations that are unlikely to improve the objective metric.

Answer

Switch the strategy from Bayesian to random search

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Use a smaller instance type for each training job

Answer

Increase the maximum number of training jobs

Question 4

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

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Custom transform with Python

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One-hot encode with threshold

Question 5

A company is using SageMaker Autopilot to automatically build a regression model on a dataset. They want to understand which features are most important for the model's predictions. Which feature of Autopilot can provide this insight?

Accepted Answer

Autopilot explainability report. SageMaker Autopilot can generate explainability reports that include feature importance, either through SHAP or other methods, depending on the model type.

Answer

Autopilot candidate definition notebook

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Autopilot model leaderboard

Answer

Autopilot data exploration report

Question 6

A data scientist is using SageMaker to train a linear regression model. After training, they evaluate the model on the test set and get an R² of 0.95. However, when they deploy the model to a SageMaker endpoint and run predictions on new data, the predictions are far off. What is the most likely cause?

Accepted Answer

The test set is not representative of the production data distribution.. A high R² of 0.95 on the test set indicates the model fits the test data well, but if the test set was drawn from the same distribution as the training data and does not reflect the real-world production data, the model will fail to generalize. In SageMaker, the endpoint serves predictions on live data that may have different statistical properties, leading to poor performance despite high test-set metrics. This is a classic case of dataset shift, not a model training or deployment configuration issue.

Answer

The endpoint is using a different inference script.

Answer

The model was trained with a wrong algorithm.

Answer

The model is overfitting the training data.

Question 7

A company deploys a real-time inference endpoint and wants to be alerted if the number of 4XX errors exceeds 10 per minute over a 5-minute period. Which steps should they take?

Accepted Answer

Create a CloudWatch alarm on the 4XXError metric with a threshold of 10 and an evaluation period of 5 minutes, and configure SNS notification. A CloudWatch alarm on the `4XXError` metric with a threshold of 10 and an evaluation period of 5 minutes directly monitors the rate of HTTP 4XX errors from the SageMaker real-time inference endpoint. When the alarm state transitions to ALARM (i.e., the average 4XX errors per minute exceeds 10 over the 5-minute window), it triggers an SNS notification to alert the team. This is the standard approach for real-time metric-based alerting in AWS.

Answer

Create a CloudWatch alarm on the Invocations metric and set a threshold

Answer

Enable endpoint auto-scaling with a target tracking policy

Answer

Use SageMaker Model Monitor to capture invocations and trigger an SNS topic

Question 8

A machine learning engineer wants to encrypt model artifacts stored in Amazon S3. The artifacts are created and used by SageMaker training jobs and endpoints. What is the simplest way to ensure encryption at rest?

Accepted Answer

Create an S3 bucket with default encryption using SSE-S3 and allow SageMaker access.. SSE-S3 provides server-side encryption with Amazon S3-managed keys, which is the simplest way to encrypt data at rest because it requires no additional key management or configuration beyond enabling default encryption on the bucket. SageMaker training jobs and endpoints can seamlessly read and write encrypted objects when the bucket has default SSE-S3 enabled, as SageMaker automatically handles the decryption during access. This approach minimizes operational overhead while meeting the encryption-at-rest requirement.

Answer

Use SageMaker's default encryption with an AWS managed key.

Answer

Enable S3 bucket versioning and MFA delete.

Answer

Use a custom KMS key and grant SageMaker permission to use it.

Question 9

A data scientist is using SageMaker Data Wrangler to prepare features for a classification model. Which TWO statements about feature engineering in Data Wrangler are correct?

Accepted Answer

Transformations created in Data Wrangler can be exported as a SageMaker Processing script. SageMaker Data Wrangler allows you to export the entire data flow, including all transformations, as a SageMaker Processing script. This script can be run at scale on managed infrastructure, enabling you to operationalize the feature engineering pipeline for training or inference without manual rework.

Answer

Data Wrangler only supports CSV and Parquet input formats

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Data Wrangler enables writing custom PySpark transformations

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Data Wrangler automatically scales features for XGBoost models

Question 10

A data scientist is preparing a dataset for a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?

Accepted Answer

Impute with mean. For a numerical feature with a normal distribution and no outliers, the mean is the most appropriate imputation method because it preserves the central tendency of the data without introducing bias. In linear regression, mean imputation maintains the expected value of the feature, which is critical for unbiased coefficient estimates when data are missing completely at random (MCAR).

Answer

Impute with mode

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Impute with median

Answer

Drop rows with missing values

Question 11

Refer to the exhibit. A team receives an error when running a SageMaker Model Monitor schedule for data quality. What should they do to resolve this issue?

Accepted Answer

Create a baseline job using the training dataset. The error occurs because SageMaker Model Monitor requires a baseline to compare against live data. Without a baseline job created from the training dataset, the monitoring schedule fails. Option D resolves this by generating the necessary statistics and constraints that define expected data quality.

Answer

Update the IAM role to allow S3 access

Answer

Restart the monitoring schedule

Answer

Enable data capture on the endpoint

Question 12

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Question 13

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 14

A data scientist is running a SageMaker training job with a custom PyTorch image. The training script loads a large dataset into memory, and the job fails with an out-of-memory error after a few minutes. The instance type is ml.m5.xlarge (16 GB RAM). What should the data scientist do to resolve this issue without changing the instance type?

Accepted Answer

Reduce the batch size in the training script. Reducing the batch size decreases the amount of data loaded into memory at once, directly addressing the out-of-memory error without changing the instance type. Since the training script loads a large dataset into memory and fails after a few minutes, a smaller batch size reduces peak memory consumption per iteration, allowing the job to fit within the 16 GB RAM of ml.m5.xlarge.

Answer

Enable SageMaker Managed Spot Training to free memory

Answer

Implement data loading with multiprocessing and increase the number of workers

Answer

Use SageMaker Pipe mode to stream data from S3

Question 15

An MLOps engineer is designing a CI/CD pipeline for deploying machine learning models to a production SageMaker endpoint. The pipeline should include automated testing, approval gates, and rollback capability. Which THREE components should be included in the pipeline? (Select THREE.)

Accepted Answer

A CloudFormation template to deploy the endpoint infrastructure, enabling rollback via stack update.. Using a CloudFormation template to deploy the SageMaker endpoint infrastructure enables rollback via stack update. If a deployment fails, CloudFormation can automatically roll back the stack to the previous known good state, ensuring infrastructure consistency and reducing downtime.

Answer

A step to register the model in SageMaker Model Registry.

Answer

A step to run SageMaker Debugger to monitor training.

Question 16

A machine learning engineer is deploying a pre-trained NLP model on Amazon SageMaker for real-time inference. The model expects input sequences of variable length, and performance is critical. The engineer wants to minimize latency while handling the variable-length inputs efficiently. Which approach should the engineer choose?

Accepted Answer

Use dynamic batching with a custom inference script that groups requests by sequence length.. Dynamic batching with a custom inference script that groups requests by sequence length minimizes padding overhead and maximizes hardware utilization. By batching similar-length sequences together, the model avoids excessive padding to the maximum length in the batch, which reduces wasted computation and latency. This approach is particularly effective for variable-length NLP inputs on SageMaker, where the inference container can be customized to implement the grouping logic.

Answer

Reduce the model size by pruning and quantization.

Answer

Pad all input sequences to the maximum length in the batch.

Answer

Process each request individually to avoid padding overhead.

Question 17

A company uses SageMaker Pipelines to automate their ML workflow. They notice that the pipeline reruns all steps even when the input data has not changed. Which feature should they enable to avoid unnecessary recomputation?

Accepted Answer

Enable pipeline caching. Pipeline caching in SageMaker Pipelines automatically reuses the output of a step if its inputs (including parameters, data, and code) have not changed since the last successful execution. This avoids recomputation by comparing a hash of the step's dependencies against previous runs, making it the correct feature to prevent unnecessary reruns when input data remains identical.

Answer

Use a Lambda step to check input changes

Answer

Use a Conditional step to skip steps

Answer

Set the pipeline execution mode to 'Parallel'

Question 18

A company ingests daily log data into an S3 bucket. They need to update the existing ML training dataset with new data without reprocessing the entire history. Which two strategies should they adopt? (Choose two.)

Accepted Answer

Use AWS Glue to incrementally process new partitions. AWS Glue can perform incremental processing by using job bookmarks to track previously processed data and only process new partitions or files. This avoids reprocessing the entire historical dataset, making it efficient for updating ML training datasets with daily log data.

Answer

Store all data in a single large file and use append operations

Answer

Manually copy new files to the same S3 bucket

Answer

Overwrite the entire existing dataset with the new data

Question 19

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

Answer

Configure SageMaker training job to publish to SNS on failure

Answer

Use EventBridge to monitor the training job status

Question 20

A data scientist is preparing data for a regression model. The target variable has a skewed distribution. The scientist wants to apply a log transformation to make it closer to normal. Which step should be taken before applying log transformation?

Accepted Answer

Ensure all values are positive. The log transformation is defined only for positive real numbers; applying it to zero or negative values results in undefined or complex outputs. Therefore, before applying a log transformation, you must ensure all values in the target variable are positive, typically by adding a constant (e.g., log(x + 1)) if zeros are present. This step is a fundamental data preparation requirement for log transformations in regression modeling.

Answer

Standardize the data to zero mean and unit variance

Answer

Remove outliers using IQR

Answer

Center the data by subtracting the mean