MLA-C01 Practice Test 3 — 30 Questions

Question 1

A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model is a gradient boosting model using XGBoost. Which SageMaker hosting configuration is MOST cost-effective while meeting the requirements?

Accepted Answer

Use a SageMaker Multi-Model Endpoint (MME) on an ml.c5.4xlarge instance with auto scaling. Option B is correct because a Multi-Model Endpoint (MME) on a single ml.c5.4xlarge instance allows multiple models to share the same endpoint, reducing cost while still meeting the latency (<10 ms) and throughput (1000 req/s) requirements. The ml.c5.4xlarge provides sufficient compute (16 vCPUs, 32 GB memory) for XGBoost inference, and auto scaling ensures capacity adjusts to handle peak load without over-provisioning.

Answer

Use SageMaker Batch Transform with multiple instances

Answer

Deploy on a single ml.c5.xlarge instance with a real-time endpoint

Answer

Deploy separate real-time endpoints for each model on ml.m5.large instances

Question 2

A data science team at a financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting classifier trained on historical transaction data. The model is deployed to a SageMaker endpoint with an ML.M5.LARGE instance for real-time inference. After deployment, the team observes that the endpoint's latency spikes to over 2 seconds during peak hours (10:00-12:00 and 14:00-16:00), causing timeouts for client applications. The average latency during off-peak hours is 200 ms. The team has enabled auto-scaling with a target average CPU utilization of 70%, but the endpoint still experiences high latency during peak hours. The instance count never scales beyond 2 instances during peaks. The model size is 500 MB, and each request includes 200 features. The team needs to reduce latency to under 500 ms at the 99th percentile during peak hours without increasing costs beyond the current budget. Which course of action should the team take?

Accepted Answer

Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference.. Option C is correct because GPU instances like ml.g4dn.xlarge are optimized for compute-intensive workloads such as gradient boosting inference, which involves numerous matrix operations. By offloading the computation to the GPU, the model can process each request faster, reducing latency from over 2 seconds to under 500 ms at the 99th percentile without increasing the instance count or budget. This directly addresses the root cause—CPU-bound inference during peak hours—while keeping costs stable.

Answer

Configure SageMaker batch transform for the real-time endpoint to process requests asynchronously.

Answer

Increase the auto-scaling maximum instance count to 10 and set target CPU utilization to 50%.

Answer

Enable data compression on the endpoint to reduce payload size and network latency.

Question 3

A machine learning engineer is developing a text classification model using Amazon SageMaker. The dataset consists of 1 million customer reviews, with labels indicating sentiment (positive, negative, neutral). The engineer uses a pre-trained BERT model from the Hugging Face Model Hub and fine-tunes it on the dataset using SageMaker's Hugging Face estimator with a ml.p3.2xlarge instance. After 2 hours of training, the training job fails with a 'ResourceExhaustedError: CUDA out of memory' error. The error occurs during the forward pass of the first epoch. The engineer confirms that the batch size is set to 32, the maximum sequence length is 512 tokens, and the dataset is stored in a S3 bucket in the same AWS region. The engineer needs to complete fine-tuning without increasing instance costs. Which course of action should the engineer take?

Accepted Answer

Reduce the batch size to 8 and enable gradient accumulation with 4 steps to maintain effective batch size.. Option A is correct because reducing the batch size to 8 directly lowers GPU memory usage per forward pass, and enabling gradient accumulation with 4 steps allows the model to simulate the original effective batch size of 32 (8 × 4 = 32) without increasing memory footprint. This approach resolves the CUDA out-of-memory error while keeping the same instance type (ml.p3.2xlarge) and without incurring additional costs.

Answer

Enable SageMaker Managed Spot Training to reduce costs and use the savings to upgrade to a ml.p3.8xlarge instance.

Answer

Switch to a CPU-based instance like ml.c5.2xlarge to avoid GPU memory constraints.

Answer

Reduce the maximum sequence length to 128 tokens to lower memory consumption.

Question 4

A machine learning engineer is deploying a model using AWS Lambda for inference. The model is a small scikit-learn classifier with a size of 50 MB. The Lambda function is invoked by an API Gateway REST API. The engineer notices that cold starts are causing high latency. Which action would most effectively reduce cold start latency without increasing costs significantly?

Accepted Answer

Configure provisioned concurrency for the Lambda function.. Option C is correct because provisioned concurrency pre-initializes the Lambda execution environment, keeping it warm and ready to handle requests immediately. This eliminates the cold start overhead for the first request, directly reducing latency without incurring the ongoing costs of a larger memory allocation or the complexity of EFS/container management.

Answer

Store the model in Amazon EFS and load it at runtime.

Answer

Increase the Lambda function memory to the maximum of 10,240 MB.

Answer

Package the model in a container image and deploy using Lambda container support.

Question 5

A team is deploying a deep learning model on a SageMaker real-time endpoint. The model has high memory requirements, and the team wants to minimize instance cost while ensuring the endpoint can handle up to 10 concurrent requests. They plan to use a single ml.p3.2xlarge instance (8 vCPUs, 61 GB memory). Which SageMaker endpoint configuration will allow the endpoint to handle 10 concurrent requests without errors?

Accepted Answer

Set the initial instance count to 1 and configure the container to use multiple ModelServerWorkers.. Option B is correct because SageMaker's ModelServerWorkers (MSWs) allow a single container to handle multiple inference requests concurrently by running multiple worker processes. With 8 vCPUs on ml.p3.2xlarge, configuring multiple MSWs (e.g., 8 workers) enables the endpoint to process up to 10 concurrent requests without errors, as each worker can handle one request at a time. This minimizes cost by using a single instance while meeting concurrency requirements.

Answer

Disable ModelServerWorkers to reduce overhead.

Answer

Set the initial variant weight to 10.

Answer

Set the initial instance count to 10 in the production variant.

Question 6

A company is deploying a machine learning model using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The company expects unpredictable traffic patterns with occasional bursts. They want to minimize cost while ensuring low latency during bursts. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Use a multi-model endpoint with a mix of CPU and GPU instances to handle variable traffic.. Option B is correct because a multi-model endpoint with a mix of CPU and GPU instances allows the company to host multiple models on the same endpoint, reducing cost by sharing underlying instances. By including GPU instances, the endpoint can handle the GPU-intensive deep learning inference for the large model, while the CPU instances can serve lighter loads or fallback traffic, ensuring low latency during unpredictable bursts without over-provisioning.

Answer

Use a serverless endpoint configuration to automatically scale.

Answer

Use Spot instances for the endpoint to reduce cost.

Answer

Provision multiple on-demand GPU instances behind a load balancer.

Question 7

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. Option B is correct because 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 8

Which TWO of the following are best practices for deploying machine learning models on SageMaker? (Select TWO.)

Accepted Answer

Use separate production and staging endpoints to test new models before full rollout. Option B and Option D are correct. Option A is wrong because model should be in S3, not EBS. Option C is wrong because you should use the SageMaker Model Registry for versioning. Option E is wrong because CloudWatch Logs are enabled by default, not disabled.

Answer

Store model artifacts in Amazon EBS volumes attached to the endpoint instances

Answer

Manually track model versions using tags because SageMaker Model Registry is not available for deployment

Answer

Disable CloudWatch Logs to reduce costs during inference

Question 9

A financial services company uses a custom container on Amazon SageMaker to serve a fraud detection model. The model's inference latency has recently increased, causing timeouts for some requests. The team reviews the SageMaker logs and finds that the container is consuming more memory than allocated. What should the team do to maintain service quality while ensuring cost-effectiveness?

Accepted Answer

Change the instance type to a memory-optimized instance, such as r5.large. The correct answer is D because the root cause is that the container is consuming more memory than allocated, leading to increased latency and timeouts. Switching to a memory-optimized instance like r5.large directly addresses the memory constraint by providing more memory per vCPU, which resolves the performance issue without over-provisioning compute resources. This approach is cost-effective because it targets the specific bottleneck (memory) rather than scaling out or changing unrelated parameters.

Answer

Decrease the model's batch size to reduce memory usage

Answer

Increase the number of instances in the endpoint to distribute the load

Answer

Implement an auto-scaling policy based on memory utilization

Question 10

A healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)

Accepted Answer

Deploy SageMaker Model Monitor to track prediction quality over time. A is correct because Amazon SageMaker Model Monitor can continuously track prediction quality metrics (e.g., accuracy, precision) over time by analyzing data captured from the endpoint. This allows the team to detect concept drift by comparing live predictions against a baseline, triggering alerts when performance degrades. It provides a managed, automated way to monitor model quality without manual intervention.

Answer

Disable the existing endpoint to prevent stale predictions during retraining

Answer

Manually compare the model's predictions against a holdout validation set each week

Question 11

A machine learning engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?

Accepted Answer

Concept drift. Option B is correct because a change in the target variable distribution is concept drift. Option A is wrong because covariate shift is input distribution change. Option C is wrong because prior probability shift is a type of concept drift, but not the best answer here. Option D is wrong because data drift is a general term.

Answer

Prior probability shift

Answer

Data drift

Answer

Covariate shift

Question 12

A team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?

Accepted Answer

Migrate to a SageMaker Serverless Inference endpoint. Option C is correct because a serverless inference endpoint scales to zero when not in use, reducing cost. Option A is wrong because multi-model endpoints still have always-on instances. Option B is wrong because reducing instances may cause throttling. Option D is wrong because asynchronous inference is for batch, not real-time.

Answer

Switch to a multi-model endpoint to share instances across models

Answer

Reduce the number of instances to one

Answer

Implement an asynchronous inference endpoint

Question 13

A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles trained models into an optimized binary for the target hardware, applying techniques like operator fusion, memory layout optimization, and quantization. This reduces model size and inference latency while preserving accuracy, making it the correct choice for addressing high latency caused by model size.

Answer

Switch to a compute-optimized instance type

Answer

Reduce the batch size of inference requests

Answer

Convert the model to ONNX format

Question 14

A company uses Amazon Rekognition to moderate user-generated images. They want to set up a monitoring system that alerts the team if the number of inappropriate images flagged by the model exceeds a threshold. Which combination of AWS services should they use?

Accepted Answer

Amazon CloudWatch to publish custom metrics and create an alarm, and AWS Lambda to process images and publish metrics.. Option A is correct because Amazon CloudWatch can publish custom metrics (number of flagged images) and trigger alarms. AWS Lambda can process the images and publish the metric. Option B is incomplete because CloudWatch Logs alone cannot trigger alarms on custom metrics. Option C is incorrect because SNS alone does not monitor metrics. Option D is incorrect because AWS Config is for configuration tracking.

Answer

Amazon CloudWatch Logs to store inference logs and create a metric filter.

Answer

AWS Config to track resource changes and trigger an SNS notification.

Answer

Amazon Simple Notification Service (SNS) to send alerts when threshold is exceeded.

Question 15

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

Accepted Answer

Parquet. Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.

Answer

XML

Answer

ORC

Answer

JSON Lines

Question 16

In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?

Accepted Answer

Import the Redshift table directly using a Data Wrangler source step and apply a join transform. Option D is correct because SageMaker Data Wrangler natively supports Amazon Redshift as a source via a direct connection, allowing you to import the Redshift table as a source step and then apply a join transform within the same visual flow. This approach avoids unnecessary data movement or intermediate exports, which is critical for hundreds of GB of data, as it leverages Data Wrangler's optimized in-memory and Spark-based processing to perform the join efficiently.

Answer

Use Amazon Athena federated query to join in place and import the result

Answer

Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join

Answer

Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler

Question 17

A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?

Accepted Answer

The `age` feature type should be `Integral`, not `String`.. Option B is correct because the feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.

Answer

The role does not have permissions to write to the feature store.

Answer

The `OnlineStoreConfig` must include a `SecurityConfig`.

Answer

The `EventTimeFeatureName` is incorrectly spelled.

Question 18

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

Accepted Answer

Random oversampling of the minority class. Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.

Answer

Principal Component Analysis (PCA) dimensionality reduction

Answer

Standard scaling of numerical features

Answer

One-hot encoding of categorical variables

Question 19

A company is building a real-time inference pipeline for an ML model. The raw data arrives in JSON format via Amazon Kinesis Data Streams. Before invoking the SageMaker endpoint, the data must be preprocessed to match the training data format. Which THREE steps should be included in the preprocessing function? (Select THREE)

Accepted Answer

Ensure that missing values are handled consistently with the training phase. Option A is correct because the preprocessing function must handle missing values identically to how they were handled during training to maintain data consistency. If the training phase used mean imputation for a numeric feature, the inference pipeline must apply the same mean value; otherwise, the model will receive unexpected input distributions, degrading prediction accuracy.

Answer

Convert the data to a CSV string for model input

Answer

Re-train the model periodically using new data

Question 20

A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?

Accepted Answer

Target encoding with smoothing using SageMaker Data Wrangler. Target encoding with smoothing in SageMaker Data Wrangler is the most scalable and effective approach because it replaces each high-cardinality category with the mean of the target variable, smoothed by a global prior to prevent overfitting. SageMaker Data Wrangler handles datasets with over 1000 unique values efficiently without exploding feature dimensions, unlike one-hot encoding, and avoids the ordinal bias of label encoding.

Answer

Hash encoding using Apache Spark on Amazon EMR

Answer

One-hot encoding using SageMaker Processing with scikit-learn

Answer

Label encoding using Pandas in a SageMaker notebook