MLA-C01 Practice Questions 2 — 50 Questions

Question 1

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?

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

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

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CloudWatch Alarm → EventBridge → SageMaker Training Job

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CloudWatch Alarm → Lambda directly (without SNS)

Question 2

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

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Use a SageMaker notebook to query Redshift and transform

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Use Amazon QuickSight to transform the timestamp

Question 3

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

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

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TFRecord files with sequence features

Question 4

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

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Use AWS Glue's built-in random split transform

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

Question 5

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

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Custom transform with Python

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Drop missing rows

Question 6

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 7

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.

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Shuffle the dataset before splitting into train and test sets.

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Manually inspect and clean data to remove outliers.

Question 8

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 9

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

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Configure a lifecycle configuration

Question 10

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.

Question 11

Refer to the exhibit. A data scientist is trying to use AWS Glue to read data from the S3 bucket `ml-data-bucket`. The Glue job fails with an access denied error. What is the most likely cause?

Accepted Answer

The policy does not include s3:ListBucket permission on the bucket. The error occurs because the IAM policy attached to the Glue job role grants s3:GetObject on the bucket objects (via the `arn:aws:s3:::ml-data-bucket/*` resource) but does not include the s3:ListBucket permission on the bucket itself (`arn:aws:s3:::ml-data-bucket`). When AWS Glue reads data from S3, it first performs a ListBucket operation to enumerate objects in the bucket or prefix, and without that permission, the request is denied even if GetObject is allowed.

Answer

The policy allows s3:PutObject but the job only reads

Answer

The policy does not specify the bucket ARN without /*

Answer

The Glue job role does not have the required permissions

Question 12

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. 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

Answer

Attach Elastic Inference to a generic container

Answer

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

Question 13

A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.

Answer

Oversampling the minority class by duplicating examples

Answer

Collect more data to match the number of samples in both classes

Answer

Random undersampling of the majority class

Question 14

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset for a regression model. After applying a StandardScaler to numeric features, the target variable has a mean of 50 and standard deviation of 20. Which additional step should the engineer take to reduce model bias?

Accepted Answer

Use SageMaker Clarify to evaluate bias in the dataset. SageMaker Clarify is specifically designed to detect various types of bias in datasets and models, including regression bias. Even after standard scaling, the target variable's distribution (mean=50, std=20) may still contain systemic biases related to sensitive attributes (e.g., race, gender). SageMaker Clarify computes bias metrics such as Conditional Demographic Disparity in Labels (CDDL) and can identify whether the model's predictions are unfairly skewed across demographic groups, which is the direct step needed to reduce model bias.

Answer

Apply MinMaxScaler to the target variable

Answer

Apply one-hot encoding to all categorical features

Answer

Remove all features with a correlation above 0.8

Question 15

A data scientist is using Amazon SageMaker Studio to develop a model. The training job is taking longer than expected. The data scientist suspects that the data is being downloaded from Amazon S3 each time the training starts. What is the BEST way to reduce data loading time?

Accepted Answer

Use SageMaker Pipe Input mode to stream data directly from S3.. SageMaker Pipe input mode streams data directly from S3 into the training algorithm without first downloading it to the training instance's local storage. This eliminates the bottleneck of copying entire datasets, reducing startup time and disk usage. It is the most direct and efficient way to address the issue of repeated downloads from S3.

Answer

Enable S3 transfer acceleration and cache the data in S3.

Answer

Use a larger instance type with more network bandwidth.

Answer

Use Amazon FSx for Lustre to mount a high-performance file system.

Question 16

A company deploys a model with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?

Accepted Answer

Concept drift; retrain the model with newly collected labeled data. This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.

Answer

Concept drift; ignore the change as long as input distribution remains stable

Answer

Data drift; update the baseline statistics and continue monitoring

Answer

Data drift; retrain the model with the latest training data

Question 17

A financial services company is deploying a credit risk model using SageMaker. They require that the model always uses the latest approved version from the Model Registry. They also need to maintain a detailed audit trail of all model version transitions (e.g., from PendingApproval to Approved). The deployment should be fully automated and must roll back immediately if the new model's error rate exceeds the old model's error rate by more than 2% during a canary deployment. Which solution meets these requirements with the least custom code?

Accepted Answer

Use SageMaker Pipelines with a conditional step to deploy the model after approval, and include a canary deployment using a weight endpoint variant. Use CloudWatch alarms to trigger automatic rollback.. SageMaker Pipelines natively supports conditional execution and canary deployments using endpoint weight variants, which together enable automated rollback triggered by CloudWatch alarms when the error rate exceeds the 2% threshold. This approach requires minimal custom code by leveraging built-in SageMaker capabilities for model registry integration, deployment, and monitoring.

Answer

Use AWS CodePipeline with a deployment action that uses AWS CloudFormation to update the endpoint. Add a manual approval step for rollback.

Answer

Create an AWS Lambda function that is triggered by Model Registry events, deploys the model to a staging endpoint, runs a canary test, and if successful, updates the production endpoint.

Answer

Use an Amazon EKS cluster with a custom inference container and use ArgoCD for automated deployments.

Question 18

A company is deploying a large NLP model on SageMaker for real-time inference. They want to reduce inference latency and cost by optimizing the model for the target hardware. The model is trained in PyTorch. Which SageMaker feature should they use to compile the model for best performance on the chosen instance?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct choice because it is specifically designed to compile trained models (including PyTorch models) into an optimized binary for a target hardware instance, reducing inference latency and improving throughput. Neo applies hardware-specific optimizations such as operator fusion, memory layout tuning, and quantization, which directly address the need for best performance on the chosen SageMaker instance.

Answer

AWS Step Functions

Answer

Amazon Elastic Inference

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SageMaker Triton Inference Server

Question 19

A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?

Accepted Answer

The pipeline can be configured to fail if data quality checks do not meet thresholds. SageMaker Pipelines allows you to define conditions that evaluate the output of data quality checks (e.g., using Amazon SageMaker Model Monitor or custom validation scripts). If the checks fail to meet specified thresholds (e.g., missing values exceed 5%), the pipeline can be configured to fail, stopping execution and preventing downstream steps from processing invalid data.

Answer

Data validation can only be performed on training data, not inference data

Answer

Data validation steps cannot pass results to subsequent steps

Answer

Data validation requires a trained model to evaluate predictions

Question 20

A data engineer is using Amazon SageMaker Data Wrangler to create a data preparation flow for a dataset with 500 columns, many of which are highly correlated. The goal is to reduce dimensionality while preserving interpretability. Which built-in transform in Data Wrangler should be applied?

Accepted Answer

Feature Selection (correlation-based). The goal is to reduce dimensionality while preserving interpretability. SageMaker Data Wrangler's built-in Feature Selection (correlation-based) transform identifies and removes highly correlated columns, directly reducing the number of features without transforming the original variables into new, uninterpretable components. This preserves the meaning of each selected column, which is essential when interpretability is a priority.

Answer

Imputation

Answer

Principal Component Analysis (PCA)

Answer

StandardScaler