MLA-C01 Practice Test 2 — 30 Questions

Question 1

A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

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

Accepted Answer

Read the Parquet files directly using SparkSession.read.parquet. SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

Answer

Read the Parquet files as text using SparkContext.textFile

Answer

Split the dataset into many small Parquet files (e.g., 1 MB each)

Answer

Convert the Parquet files to CSV before processing

Question 2

A financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting model (XGBoost) trained on historical transaction data. The inference endpoint uses an ml.m5.2xlarge instance with a single variant. Recently, the company has experienced a 3x increase in transaction volume during peak hours, causing inference latency to exceed the 200ms SLA. The data science team has already optimized the model by reducing the number of trees and feature set, but the latency remains high during spikes. The team considers using SageMaker's built-in scaling policies. They currently have a single endpoint with one production variant. The team wants to maintain low latency without over-provisioning resources. They have ruled out model changes. Which approach should the team take?

Accepted Answer

Configure an Application Auto Scaling target tracking scaling policy for the variant based on the 'SageMakerVariantInvocationsPerInstance' metric, with a target value that keeps the inference latency within the SLA.. SageMaker's built-in target tracking scaling policy using the 'SageMakerVariantInvocationsPerInstance' metric allows the endpoint to automatically adjust the instance count based on real-time invocation load. By setting a target value that correlates with the 200ms SLA, the policy dynamically scales out during traffic spikes and scales in during lulls, preventing over-provisioning while maintaining low latency. This approach directly addresses the 3x peak-hour volume increase without requiring manual intervention or model changes.

Answer

Deploy the model on multiple endpoints behind an Application Load Balancer.

Answer

Use scheduled scaling to increase the instance count during known peak hours.

Answer

Manually increase the instance count during peak hours.

Question 3

A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?

Accepted Answer

AUC. AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.

Answer

Precision

Answer

Accuracy

Answer

Recall

Question 4

A company wants to track the lineage of their ML models, including the training dataset, hyperparameters, and training job used to produce each model version. Which AWS service should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct choice because it is purpose-built to record and query the provenance of ML models, capturing relationships between datasets, training jobs, hyperparameters, and model versions. It creates a directed acyclic graph (DAG) of entities (e.g., artifacts, actions, contexts) that allows you to trace how a specific model version was produced, which directly meets the requirement for lineage tracking.

Answer

Amazon DynamoDB

Answer

AWS Glue Data Catalog

Answer

Amazon S3 object tagging

Question 5

A financial services company deploys a fraud detection model on a SageMaker real-time endpoint. The inference logic includes a pre-processing step that requires access to a DynamoDB table for user metadata. The model container is a custom Docker image. How should the team grant the endpoint access to DynamoDB?

Accepted Answer

Create an IAM role with DynamoDB read access and assign it to the SageMaker endpoint as the execution role. SageMaker endpoints require an IAM execution role to be assigned at creation time. This role defines the permissions the endpoint's container has when making AWS API calls, such as reading from DynamoDB. By attaching a policy with DynamoDB read access to this execution role, the endpoint securely obtains temporary credentials via the AWS STS service, eliminating the need to hardcode or manage long-term credentials.

Answer

Store IAM credentials in the container image as environment variables

Answer

Attach an IAM instance profile to the underlying EC2 instance

Answer

Retrieve temporary credentials from AWS Secrets Manager within the container code

Question 6

A company wants to use a pre-trained NLP model from SageMaker JumpStart for sentiment analysis. Which step is required to make predictions?

Accepted Answer

Deploy the model to an endpoint. D is correct because SageMaker JumpStart provides pre-trained models that are ready for inference without additional training. To make predictions, you must deploy the model to a SageMaker endpoint, which creates a hosted inference endpoint that can accept input data and return sentiment analysis results.

Answer

Label the dataset for fine-tuning

Answer

Train the model from scratch on the company's data

Answer

Convert the model to ONNX format

Question 7

Refer to the exhibit. A data scientist ran a training job using a custom algorithm container. The job failed with the error shown. What is the most likely cause?

Accepted Answer

The algorithm script references an undefined variable or metric named 'loss'. The error 'Cannot evaluate expression: loss' indicates that the training script attempted to compute or log a variable named 'loss' that is not defined in the code. The training image access, S3 output path, and instance type are not related to this specific error.

Answer

The S3 output path is incorrect

Answer

The training image is not accessible

Answer

The instance type is insufficient

Question 8

A financial services company is building a fraud detection model using transactional data stored in Amazon S3. The data includes transaction_id, timestamp, amount, merchant_category, and fraud_label (0/1). The data is collected from multiple sources and has inconsistencies: timestamps are in different timezones (UTC and EST), merchant categories are sometimes misspelled (e.g., 'RESTAURANT', 'Restaurant', 'restaurant'), and the fraud_label is missing for about 5% of records. The data science team uses AWS Glue for ETL. They need to prepare a clean dataset for training. The final dataset must have consistent timestamps in UTC, standardized merchant categories, and no missing fraud labels. The team also wants to minimize data loss. Which set of actions should the team take?

Accepted Answer

Use AWS Glue to convert timestamps to UTC, use a mapping table to group similar merchant categories (e.g., all restaurant variants to 'Restaurant'), and impute missing fraud_label using mode (most frequent value).. It preserves data by imputing missing fraud labels using the mode (most frequent value), which is appropriate for a binary classification label where the majority class is likely 0. It also standardizes timestamps to UTC and uses a mapping table to group merchant category variants, ensuring consistency without data loss. Dropping records (as in A and C) would reduce the dataset size, and imputing with the mean (as in B) is invalid for a categorical label.

Answer

Use AWS Glue to convert all timestamps to UTC, apply a mapping function to correct merchant category misspellings to a standard list, and drop records with missing fraud_label.

Answer

Use AWS Glue to convert timestamps to UTC, use a fuzzy matching algorithm to standardize merchant categories, and replace missing fraud_label with the mean value (0.05).

Answer

Use AWS Glue to convert timestamps to UTC, correct merchant categories by mapping known misspellings to correct names, and drop records with missing fraud_label.

Question 9

A company has a SageMaker endpoint running a model that provides real-time recommendations. Recently, the model's accuracy has degraded due to data drift. The team wants to automatically retrain the model when a drift metric exceeds a threshold and deploy the new model without downtime. Which architecture should the team implement?

Accepted Answer

Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant. It uses SageMaker Model Monitor to detect data drift and emit an EventBridge event, which triggers a SageMaker Pipeline to retrain the model, register it in the Model Registry, and then update the existing endpoint with a new production variant. This architecture enables automatic retraining and zero-downtime deployment by leveraging the endpoint's production variants for a blue/green deployment.

Answer

Use SageMaker Model Monitor to collect drift metrics, and have a data scientist manually analyze the metrics and trigger retraining via the SageMaker console

Answer

Schedule a daily SageMaker Pipeline that retrains the model and deploys it using a new endpoint, then updates the application to point to the new endpoint

Answer

Use SageMaker Model Monitor to publish drift metrics to Amazon CloudWatch, and create a CloudWatch alarm that triggers an AWS Lambda function to retrain and deploy the model

Question 10

A data scientist is performing text preprocessing for a sentiment analysis model. The dataset contains many stop words and rare words. Which combination of preprocessing steps will reduce dimensionality and improve model performance?

Accepted Answer

Tokenization, stop-word removal, and TF-IDF. Tokenization, stop-word removal, and TF-IDF vectorization reduce dimensionality by filtering common words and weighting important terms. Lemmatization can further reduce sparsity.

Answer

Remove all words shorter than 3 characters and apply label encoding

Answer

Only tokenization without any removal

Answer

Tokenization and one-hot encoding of words

Question 11

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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