MLA-C01 Practice Exam 3 — 20 Questions

Question 1

A company wants to version and track ML models, with an approval workflow for promoting models from staging to production. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized repository to catalog, version, and manage ML models, and it supports approval workflows (e.g., PendingApproval, Approved, Rejected) to promote models from staging to production. This directly addresses the requirement for version tracking and an approval gate for model promotion.

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Answer

SageMaker Pipelines

Question 2

A company stores its model training data in Amazon S3. To meet compliance requirements, all data in transit between the S3 bucket and SageMaker must be encrypted. What should the company enforce?

Accepted Answer

Enforce HTTPS for all S3 access. Enforcing HTTPS for all S3 access ensures that data in transit between the S3 bucket and SageMaker is encrypted using TLS. This meets the compliance requirement for encrypting data in transit, as HTTPS uses TLS to protect data as it travels over the network.

Answer

Enable S3 versioning

Answer

Enable S3 access logging

Answer

Use S3 server-side encryption (SSE-S3)

Question 3

An organization uses SageMaker Studio and needs to restrict Studio's internet access while allowing users to install custom packages from a private PyPI mirror hosted in a VPC. Which networking configuration should they use?

Accepted Answer

Disable internet access for Studio and configure VPC-only mode, then use a VPC endpoint to the private PyPI mirror. SageMaker Studio in VPC-only mode (no internet access) combined with VPC-only mode for the domain and VPC endpoints to private PyPI mirror enables package installation from the private mirror. NAT gateway or internet gateway would allow internet access, which is not desired.

Answer

Use a NAT gateway to allow outbound traffic to the private PyPI mirror

Answer

Disable internet access for Studio and rely on SageMaker's default VPC configuration

Answer

Enable internet access for Studio and use a VPC endpoint to the private PyPI mirror

Question 4

Which TWO actions are recommended best practices for securing an Amazon SageMaker notebook instance? (Select TWO.)

Accepted Answer

Use AWS KMS to encrypt the notebook instance's storage volume.. Encrypting the notebook instance's storage volume with AWS KMS ensures data-at-rest protection, which is a fundamental security best practice. SageMaker notebook instances use Amazon EBS volumes for storage, and KMS encryption safeguards sensitive code, datasets, and model artifacts stored on that volume against unauthorized access.

Answer

Use network ACLs to restrict API calls to the SageMaker API.

Answer

Enable Multi-AZ deployment for the notebook instance.

Answer

Associate the notebook instance with a public subnet that has an internet gateway.

Question 5

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?

Accepted Answer

Apply Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.. SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the severe class imbalance (95% class A, 5% class B) by creating a more balanced training dataset. This technique is particularly effective for tabular data in Amazon SageMaker, as it increases the representation of the minority class without simply duplicating existing samples, thereby reducing overfitting and improving the model's ability to learn decision boundaries for the minority class.

Answer

Apply data augmentation to the majority class by adding noise.

Answer

Use a weighted loss function during training to penalize misclassifications of the minority class.

Answer

Apply random under-sampling to reduce the majority class to match the minority class size.

Question 6

A data engineer is setting up a Glue ETL job to process a large dataset stored in Amazon S3. The job needs to read data in Parquet format, apply a filter, and write the results back to S3 in Parquet. The engineer wants to minimize the cost and runtime. Which optimization technique is MOST effective?

Accepted Answer

Use column pruning to read only the columns needed in the transformation.. Using column pruning (reading only necessary columns) reduces the amount of data scanned, which directly reduces I/O and cost, especially in Parquet format. Other options are also optimizations but column pruning typically has the biggest impact for wide datasets.

Answer

Increase the number of DPUs to the maximum allowed.

Answer

Convert the data to JSON format for faster read performance.

Answer

Use a single large file instead of partitioning.

Question 7

A data scientist is training a deep learning model using SageMaker and wants to use distributed training across multiple GPUs to reduce training time. Which TWO actions should the scientist take to configure distributed training? (Select TWO.)

Accepted Answer

Use the SageMaker distributed data parallelism library. The SageMaker distributed data parallelism library (option B) automatically partitions training data and synchronizes gradients across multiple GPUs, reducing training time without manual data splitting. Configuring the SageMaker estimator with a distribution parameter (option D) enables this library by specifying the distribution strategy (e.g., 'torch_distributed' or 'tensorflow_distributed'), which is required to activate distributed training.

Answer

Reduce the number of epochs to match the number of GPUs

Answer

Manually split the training data into shards and upload to S3

Answer

Set the instance count to 1 with a multi-GPU instance

Question 8

A machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?

Accepted Answer

Covariate shift. B is correct because covariate shift occurs when the distribution of the input features changes while the relationship between features and the target remains the same. In this scenario, the introduction of a new product category with a different distribution alters the input data distribution, causing the model to encounter unseen patterns and degrade in prediction accuracy.

Answer

Concept drift

Answer

Data leakage

Answer

Model decay

Question 9

A company uses Amazon SageMaker to train and deploy machine learning models. The security team requires that all data in transit between the training job and S3 be encrypted, and that no data traverses the public internet. Which configuration should the company use?

Accepted Answer

Create a VPC with S3 VPC endpoints, attach a VPC-only policy to the SageMaker execution role, and enable KMS encryption for training jobs.. It ensures that data in transit between SageMaker and S3 stays within the AWS network and is encrypted. By creating a VPC with S3 VPC endpoints, traffic uses AWS private IPs and never traverses the public internet. Attaching a VPC-only policy to the SageMaker execution role restricts the training job to only use VPC endpoints, and enabling KMS encryption for the training job ensures data is encrypted in transit (via TLS) and at rest.

Answer

Use an S3 bucket with SSE-S3 encryption and restrict bucket access to a VPC.

Answer

Enable default encryption on the S3 bucket and use HTTPS for all SageMaker endpoints.

Answer

Create a VPC with a NAT gateway, and configure SageMaker to use the VPC and enforce HTTPS.

Question 10

A financial institution uses SageMaker to train and deploy models. They need to track every experiment, model version, and deployment step for audit purposes. Which SageMaker feature should they use to capture the full lineage of artifacts, actions, and contexts?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models), actions (training jobs, deployment), and contexts (experiments). It provides a complete audit trail. Experiments alone track trials but not lineage. Model Registry tracks model versions but not full pipeline lineage. Clarify is for bias monitoring.

Answer

SageMaker Clarify

Answer

SageMaker Model Registry

Answer

SageMaker Experiments

Question 11

A machine learning engineer is setting up a retraining pipeline that triggers when concept drift is detected. They plan to use CloudWatch Alarms to monitor the model's accuracy metric. When drift is detected, they want to automatically start a SageMaker training job. Which architecture should they use?

Accepted Answer

CloudWatch Alarm → SNS → Lambda → SageMaker Training Job. CloudWatch Alarms cannot directly invoke SageMaker training jobs; they require an intermediary like SNS to trigger a Lambda function, which then calls the SageMaker API to start the training job. This pattern ensures reliable decoupling and allows the Lambda function to handle any preprocessing or conditional logic before launching the job.

Answer

CloudWatch Alarm → SQS → Lambda → SageMaker Training Job

Answer

CloudWatch Alarm → EventBridge → SageMaker Training Job

Answer

CloudWatch Alarm → Lambda directly (without SNS)

Question 12

A data scientist is exploring data stored in an Amazon Redshift cluster. The data includes timestamp columns with different formats. The scientist wants to create a new column that standardizes the timestamp format to UTC. Which approach is MOST efficient?

Accepted Answer

Use a SELECT with CONVERT_TIMEZONE in Redshift and export to S3. `CONVERT_TIMEZONE` in Amazon Redshift is a native SQL function that directly converts timestamps to UTC without moving data outside the cluster. This approach avoids the overhead of external services, leverages Redshift's massively parallel processing (MPP) engine, and is the most efficient for in-database transformations.

Answer

Use AWS Glue to read the Redshift table and apply a custom transform

Answer

Use a SageMaker notebook to query Redshift and transform

Answer

Use Amazon QuickSight to transform the timestamp

Question 13

A team is building a machine learning model for natural language processing using SageMaker BlazingText. The data preparation step must format the training data correctly. What format does BlazingText require for supervised text classification?

Accepted Answer

Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product'). BlazingText for supervised text classification expects the training data in a specific format where each line contains the text and its labels, with labels prefixed by '__label__'. This format allows BlazingText to efficiently parse and process the data for training the word2vec or classification model without additional preprocessing. Option C correctly describes this format, where the label and text are space-separated on a single line.

Answer

One-hot encoded feature vectors stored in CSV

Answer

JSON lines with a 'text' and 'label' field

Answer

TFRecord files with sequence features

Question 14

A data engineer is using AWS Glue to prepare a dataset for ML. The engineer wants to split the dataset into training and testing sets while preserving the distribution of the target variable. Which TWO methods achieve this goal? (Select TWO)

Accepted Answer

Use the `train_test_split` function from scikit-learn in a SageMaker notebook. The `train_test_split` function from scikit-learn supports the `stratify` parameter, which preserves the distribution of the target variable when splitting a dataset into training and testing sets. This is a standard, reliable method for stratified splitting in Python-based ML workflows, and it can be used directly in a SageMaker notebook.

Answer

Use Amazon Athena to create views with random sampling

Answer

Use AWS Glue's built-in random split transform

Answer

Use Amazon SageMaker's built-in SplitType parameter in a Processing Job

Question 15

A data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?

Accepted Answer

Handle missing values (with median strategy). Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing custom code.

Answer

Replace missing with constant

Answer

Custom transform with Python

Answer

Drop missing rows

Question 16

A healthcare company deploys a model that predicts patient readmission risk. The model is deployed using a SageMaker real-time endpoint with data capture enabled. The compliance team requires that all inference data be encrypted at rest in S3 using AWS KMS with a customer managed key. The team has configured the endpoint to use an IAM role that includes the necessary KMS permissions. However, after deployment, the captured data is not being written to the S3 bucket. The team checks the CloudWatch logs for the endpoint and finds no errors. The S3 bucket policy is as follows:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Principal": "*",
      "Action": "s3:PutObject",
      "Resource": "arn:aws:s3:::my-bucket/*",
      "Condition": {
        "Bool": {
          "aws:SecureTransport": "false"
        }
      }
    }
  ]
}

The bucket also has a default KMS key. What is the MOST likely reason that the captured data is not being written?

Accepted Answer

The KMS key policy does not grant the SageMaker execution role the kms:GenerateDataKey permission.. SageMaker data capture encrypts captured data at rest in S3 using server-side encryption with AWS KMS (SSE-KMS). When a customer managed KMS key is used, the SageMaker execution role must have the kms:GenerateDataKey permission to encrypt the data before writing it to S3. Even if the IAM role has other KMS permissions, without kms:GenerateDataKey, the data capture write operation fails silently, and CloudWatch logs may not show errors because the failure occurs at the KMS encryption step before the S3 PutObject call.

Answer

The bucket policy includes an explicit deny that overrides any allow.

Answer

The bucket policy denies all PutObject requests because aws:SecureTransport is false.

Answer

The S3 bucket does not exist.

Question 17

A data team is preparing data for a machine learning pipeline. Which TWO practices are best for ensuring data quality and reproducibility? (Choose two.)

Accepted Answer

Implement automated data validation checks to catch anomalies in new data.. Automated data validation checks (e.g., using AWS Glue DataBrew or Deequ on Amazon EMR) proactively catch schema drift, missing values, and distribution anomalies in new data, ensuring that only high-quality data enters the ML pipeline. This practice is essential for maintaining data quality at scale without manual intervention.

Answer

Use a fixed random seed when sampling data to ensure repeatability.

Answer

Shuffle the dataset before splitting into train and test sets.

Answer

Manually inspect and clean data to remove outliers.

Question 18

A company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?

Accepted Answer

Resample data to a fixed frequency. Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.

Answer

Remove missing timestamps

Answer

Sort data by device ID

Answer

Impute missing values with forward fill

Question 19

A company wants to deploy a trained model to a SageMaker endpoint with automatic scaling based on traffic. Which TWO configurations are required? (Choose two.)

Accepted Answer

Set up an Application Auto Scaling policy. Application Auto Scaling is the AWS service that automatically adjusts the number of instances for a SageMaker endpoint based on demand. You define a scaling policy (e.g., target tracking, step scaling) that tells Auto Scaling when to add or remove instances, which is essential for handling variable traffic without manual intervention.

Answer

Use a multi-model endpoint

Answer

Enable data capture

Answer

Configure a lifecycle configuration

Question 20

A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?

Accepted Answer

Use AWS Glue to create a crawler and ETL job that converts CSV to Parquet.. AWS Glue is the correct choice because it provides a serverless, pay-per-use ETL service that can automatically convert CSV to Parquet without writing custom code. The Glue crawler infers the schema, and the ETL job uses built-in transforms to efficiently handle 10 TB of data with minimal cost, as it only charges for the resources consumed during the job execution.

Answer

Use SageMaker Processing with a TensorFlow script to read CSV and write Parquet.

Answer

Use Amazon S3 Select to convert the data to Parquet during retrieval.

Answer

Use Amazon EMR with a Spark job to convert the files.