MLS-C01 Mock Exam 73 — 25 Questions

Question 1

A team trained a multiclass classification model using SageMaker built-in XGBoost. The model's accuracy is high, but for a specific class, recall is very low. The team wants to improve recall for that class without significant accuracy drop. Which approach is MOST effective?

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

Accepted Answer

Resample the training data to balance the class representation. Resampling the training data to balance class representation directly addresses the root cause of low recall for a specific class in a multiclass XGBoost model. XGBoost's built-in objective functions (e.g., 'multi:softmax') optimize for overall accuracy, which can bias the model toward majority classes; resampling (e.g., oversampling the minority class or undersampling the majority) forces the model to learn decision boundaries that better capture the minority class, improving recall without drastically reducing overall accuracy.

Answer

Add more training data from all classes

Answer

Increase the max_depth hyperparameter of XGBoost

Answer

Switch from XGBoost to a linear learner

Question 2

A data scientist is performing hyperparameter tuning using Amazon SageMaker Automatic Model Tuning (AMT). The job uses a random search strategy. After 20 training jobs, the best objective metric value has plateaued. The data scientist wants to explore more of the hyperparameter space. Which action should the data scientist take?

Accepted Answer

Change the tuning strategy from Random to Bayesian.. Changing the tuning strategy from Random to Bayesian allows the tuning job to use previous results to guide the search toward more promising hyperparameter regions, which can explore the space more efficiently after plateauing. Option B is incorrect because enabling early stopping terminates underperforming trials early but does not alter the search strategy itself; it may even reduce exploration. Option C is incorrect because decreasing the maximum number of training jobs reduces the total exploration of the hyperparameter space. Option D is incorrect because increasing the number of parallel training jobs only speeds up the process but still uses the same random search strategy, which is unlikely to escape the plateau.

Answer

Enable early stopping.

Answer

Decrease the maximum number of training jobs.

Answer

Increase the number of parallel training jobs.

Question 3

A company is using Amazon S3 as a data lake. The data engineering team needs to catalog the schema of the data and make it available for querying with Amazon Athena. Which AWS Glue component should be used?

Accepted Answer

AWS Glue Crawlers. AWS Glue Crawlers automatically scan data in Amazon S3, infer the schema, and populate the AWS Glue Data Catalog with metadata tables. This makes the data immediately available for querying with Amazon Athena without manual schema definition.

Answer

AWS Glue Studio

Answer

AWS Glue ETL jobs

Answer

AWS Glue DataBrew

Question 4

A data engineering team needs to orchestrate a complex workflow that involves multiple AWS Glue jobs, Lambda functions, and S3 operations. The workflow must run on a schedule and allow monitoring of each step. Which AWS service should they use?

Accepted Answer

AWS Step Functions. AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services, including AWS Glue jobs, Lambda functions, and S3 operations, into a state machine workflow. It provides built-in scheduling via Amazon EventBridge (formerly CloudWatch Events) and offers visual monitoring, logging, and error handling for each step, making it the ideal choice for complex, multi-step workflows that require observability.

Answer

Amazon Simple Workflow Service (SWF)

Answer

AWS Data Pipeline

Answer

Amazon CloudWatch Events

Question 5

A machine learning engineer is deploying a model that was trained on a large dataset stored in Amazon S3. The model needs to be retrained daily with new data. Which approach is the MOST cost-effective for storing the training data while allowing quick access for retraining?

Accepted Answer

Use S3 Intelligent-Tiering. S3 Intelligent-Tiering is the most cost-effective choice because it automatically moves data between access tiers (frequent, infrequent, and archive instant access) based on changing access patterns. Since the model is retrained daily, the training data will be accessed frequently during retraining but may have low access at other times, and Intelligent-Tiering optimizes storage costs without performance impact by charging a small monitoring fee per object.

Answer

Store all data in S3 Standard

Answer

Use S3 Glacier Deep Archive

Answer

Use S3 One Zone-IA

Question 6

A machine learning engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take to potentially improve the result?

Accepted Answer

Increase the total number of training jobs to 50.. Increasing the total number of training jobs from 20 to 50 gives the Bayesian optimization algorithm more opportunities to explore the hyperparameter space and exploit promising regions. With only 10 jobs completed, the tuning job may not have converged to the global minimum of MAE, and additional jobs can refine the search, especially since Bayesian search builds a probabilistic model that improves with more observations.

Answer

Set early stopping to avoid overfitting.

Answer

Change the objective metric to RMSE.

Answer

Switch the tuning strategy from Bayesian to Random search.

Question 7

A company uses Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The data is organized by year/month/day/hour. The team needs to ensure that all data is encrypted at rest in S3 using an AWS KMS customer managed key (CMK). Which configuration should the team implement?

Accepted Answer

In the Firehose delivery stream configuration, enable S3 destination encryption and select the customer managed KMS key.. The correct approach. Kinesis Data Firehose can be configured to encrypt data at rest in S3 using AWS KMS. In the Firehose delivery stream configuration, under S3 destination settings, you can enable encryption and select a customer managed KMS key. This ensures all data written to S3 is encrypted with that key. Option A is incorrect because S3 default encryption applies to objects uploaded directly to S3, but Firehose writes objects using its own IAM role and can override default encryption; configuring encryption in Firehose is the recommended way. Option B is incorrect because using a Lambda function to encrypt after delivery adds unnecessary complexity and latency; Firehose can encrypt natively. Option D is incorrect because a bucket policy denying PutObject without the correct KMS key would work but is not the simplest or most straightforward configuration; Firehose can handle encryption directly.

Answer

Configure the S3 bucket's default encryption to use the customer managed KMS key.

Answer

Use an AWS Lambda function to encrypt the data after it is delivered to S3.

Answer

Add a bucket policy that denies PutObject unless the request includes the correct KMS key.

Question 8

A machine learning team is using SageMaker Processing jobs to run feature engineering on large datasets. The job takes a long time to complete. Which change would most likely reduce the processing time?

Accepted Answer

Increase the number of instances in the processing cluster. Increasing the number of instances in the processing cluster enables SageMaker Processing to distribute the workload across multiple nodes, leveraging parallel processing to reduce the overall execution time. SageMaker Processing uses a distributed computing model where each instance processes a subset of the data, so adding more instances directly increases parallelism and throughput for embarrassingly parallel tasks like feature engineering.

Answer

Switch to local mode to avoid network overhead

Answer

Change the processing script from Python to PySpark

Answer

Use a larger instance type, e.g., from r5.xlarge to r5.24xlarge

Question 9

A data scientist is using Amazon SageMaker to train a model and wants to track experiments, including parameters and metrics. Which THREE actions should be taken? (Choose three.)

Accepted Answer

Create an experiment in SageMaker Experiments.. SageMaker Experiments is the native service for organizing, tracking, and comparing machine learning training runs. It provides a structured way to create experiments, trials, and trial components, enabling systematic tracking of parameters and metrics without manual overhead.

Answer

Use SageMaker Studio to manually record experiments.

Answer

Use Amazon CloudWatch Logs to store experiment data.

Question 10

A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The labeling job is taking longer than expected. The team notices that many workers are spending a lot of time on images with no objects. Which labeling strategy should they use to reduce costs and time?

Accepted Answer

Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling.. It introduces a two-stage labeling workflow: first, workers perform a quick binary classification to identify images containing objects, and only those positive images proceed to the expensive, time-consuming bounding box annotation. This directly reduces the cost and time spent on empty images, which is the root cause of the delay.

Answer

Use a private workforce instead of public.

Answer

Use automated data labeling with a pre-trained model to filter empty images.

Answer

Increase the number of workers per dataset object.

Question 11

A company uses SageMaker to host a model for real-time predictions. The model is updated weekly. To minimize downtime during model updates, what should the company do?

Accepted Answer

Create a new endpoint configuration with the new model and update the endpoint to use the new configuration. SageMaker allows you to create a new endpoint configuration with the updated model and then update the existing endpoint to use this new configuration. This triggers a rolling update where SageMaker gradually shifts traffic from the old model to the new one, ensuring zero downtime during the transition. The endpoint remains available throughout the process, and you can roll back quickly if needed by reverting to the previous configuration.

Answer

Create a second endpoint with the new model and use an Application Load Balancer to route traffic

Answer

Update the existing endpoint configuration with the new model URL

Answer

Delete the existing endpoint and create a new one with the updated model

Question 12

A data scientist is training a deep learning model using TensorFlow on Amazon SageMaker. The training job uses a single GPU instance but the GPU utilization is low. Which action is MOST likely to improve GPU utilization?

Accepted Answer

Increase the batch size. Increasing the batch size allows the GPU to process more data in parallel per training step, which keeps the GPU compute units busier and reduces idle time. In TensorFlow on SageMaker, a small batch size can cause the GPU to finish computation quickly and then wait for the next batch to be loaded, leading to low utilization. This is the most direct way to improve GPU throughput without changing the instance or model architecture.

Answer

Use a smaller instance type

Answer

Add more features

Answer

Decrease the number of epochs

Question 13

A data scientist creates a model resource in SageMaker using the JSON configuration in the exhibit. When creating an endpoint, the deployment fails with an error 'ModelError: Cannot find inference code'. What is the MOST likely cause?

Accepted Answer

The inference container environment does not specify SAGEMAKER_PROGRAM. The error 'Cannot find inference code' occurs because SageMaker requires the `SAGEMAKER_PROGRAM` environment variable in the inference container to specify the entry-point script (e.g., `inference.py`) inside the `model.tar.gz`. Without this variable, SageMaker does not know which script to execute for inference, causing the deployment to fail. Option C correctly identifies this missing environment variable as the root cause.

Answer

The model.tar.gz file is missing the model weights

Answer

The ECR image does not exist

Answer

The training container does not have the SAGEMAKER_PROGRAM variable

Question 14

Which TWO actions can reduce inference latency for a SageMaker real-time endpoint? (Choose 2.)

Accepted Answer

Choose a larger instance type with more compute capacity.. Choosing a larger instance type with more compute capacity (Option A) reduces inference latency because it provides more CPU/GPU and memory resources, enabling the model to process each request faster. This directly decreases the time per inference, which is the primary driver of latency for real-time endpoints.

Answer

Add more instances behind the endpoint.

Answer

Use batch transform instead.

Answer

Switch to asynchronous inference.

Question 15

A data scientist is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking too long due to high I/O latency waiting for data to be downloaded from S3. Which action would MOST effectively reduce the I/O latency?

Accepted Answer

Use Pipe mode for the training channel. Pipe mode streams data directly from S3 into the training algorithm without writing to disk, eliminating the I/O latency caused by downloading files to the local storage. This is the most effective solution because the bottleneck is data transfer from S3, and Pipe mode reduces it to near-zero latency by feeding data on the fly.

Answer

Use File mode for the training channel

Answer

Increase the number of training instances

Answer

Use Amazon SageMaker Elastic Inference

Question 16

A company wants to serve predictions from a model using a REST API with low latency. Which SageMaker deployment option is most appropriate?

Accepted Answer

SageMaker real-time endpoint. SageMaker real-time endpoints are designed for low-latency inference, deploying the model behind an HTTPS endpoint that autoscales to handle request traffic. This directly meets the requirement for serving predictions via a REST API with minimal latency, as the endpoint keeps the model loaded and ready to respond to individual requests in milliseconds.

Answer

SageMaker Notebook instance

Answer

SageMaker Processing job

Answer

SageMaker Batch Transform

Question 17

A company is building a recommendation system using Amazon SageMaker. The data is stored in a large S3 bucket with millions of small CSV files. The team wants to train a factorization machines model. Which data ingestion strategy will be MOST efficient?

Accepted Answer

Use a SageMaker Processing job with a Spark container to read the files and write a single RecordIO file.. SageMaker's factorization machines algorithm requires data in RecordIO-wrapped protobuf format for optimal performance, especially with high-dimensional sparse data. Using a SageMaker Processing job with Spark efficiently reads millions of small CSV files from S3, coalesces them into a single or few large RecordIO files, and avoids the overhead of many small S3 GET requests during training, which would otherwise cause severe I/O bottlenecks.

Answer

Use Amazon Athena to query the data and output to a single CSV.

Answer

Point the training job directly to the S3 bucket containing the CSV files.

Answer

Use SageMaker Data Wrangler to create a data flow and export to a training dataset.

Question 18

A company is using Amazon SageMaker to train a large natural language processing model. The training job uses a GPU instance and is expected to take several hours. The data scientist wants to monitor GPU utilization in real-time. Which approach is MOST effective?

Accepted Answer

Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console. Amazon SageMaker automatically publishes GPU utilization metrics (e.g., `GPUUtilization`, `GPUMemoryUtilization`) to Amazon CloudWatch for training jobs running on GPU instances. By enabling CloudWatch metrics (which is the default behavior for SageMaker training jobs), the data scientist can view real-time GPU utilization directly in the CloudWatch console without any code modifications. This is the most effective approach because it requires no changes to the training script and provides native, real-time monitoring.

Answer

Use SageMaker Managed Spot Training to reduce cost and monitor utilization via spot instance status

Answer

Modify the training script to periodically log GPU utilization to a file in S3

Answer

Use SageMaker Debugger to capture GPU utilization tensors

Question 19

A data engineer needs to automate the transformation of CSV files to Parquet format as soon as they are uploaded to an S3 bucket. The transformed files should be stored in another S3 bucket. Which solution is the most cost-effective and requires the least maintenance?

Accepted Answer

Configure an S3 event notification to invoke a Lambda function.. AWS Lambda, triggered by S3 event notifications, provides a serverless, event-driven architecture that automatically converts CSV to Parquet upon file upload. This approach is cost-effective because you pay only for compute time during execution, and it requires minimal maintenance as AWS manages the infrastructure, scaling, and fault tolerance.

Answer

Configure an S3 event notification to invoke an AWS Glue job.

Answer

Run an Amazon EMR cluster continuously to watch for new files.

Answer

Set up an EC2 instance with a cron job to poll the S3 bucket.

Question 20

A company is building a recommendation system for an e-commerce platform. The system needs to suggest products to users based on past purchases and browsing history. Which approach would be most appropriate for this use case?

Accepted Answer

Collaborative filtering using past user-item interactions. Collaborative filtering is the most appropriate approach because it leverages past user-item interactions (e.g., purchases, clicks) to identify patterns and recommend items that similar users have liked. This method directly captures user behavior and preferences without requiring explicit product metadata, making it ideal for e-commerce recommendation systems where implicit feedback is abundant.

Answer

Content-based filtering using product descriptions

Answer

K-means clustering of users based on demographics

Answer

Matrix factorization on user-item ratings