MLS-C01 Practice Test 4 — 25 Questions

Question 1

A team is using Amazon SageMaker to train a model on a dataset that is 500 GB in size, stored as CSV files in S3. The training job takes 2 hours using a single ml.p3.2xlarge instance. The team wants to reduce training time to under 30 minutes. The model architecture supports distributed training. Which solution will achieve this goal with the LEAST amount of code changes?

Accepted Answer

Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.. Option C is correct because SageMaker's distributed data parallelism library automatically shards the training data across multiple ml.p3.2xlarge instances, enabling parallel gradient computation and reducing wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. The model architecture already supports distributed training, so the library handles the communication and synchronization (e.g., AllReduce) transparently.

Answer

Use managed spot training to reduce cost and then use cost savings to train with a larger instance.

Answer

Use a single ml.p3.16xlarge instance with more GPUs and memory.

Answer

Change the input mode to Pipe mode to stream data from S3 directly, reducing I/O wait time.

Question 2

A company is building a real-time anomaly detection system for network traffic logs. The logs are ingested via Amazon Kinesis Data Streams and processed with an Amazon SageMaker endpoint for inference. The team needs to ensure that the inference results are stored durably and can be replayed for model retraining. The system must handle at least 10,000 records per second with low latency. Which three AWS services should the team use to build this architecture? (Select THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the correct ingestion layer because it provides durable, real-time data streaming with the ability to handle over 10,000 records per second. It acts as the source of truth for network traffic logs, enabling low-latency processing and replay for model retraining.

Answer

AWS Glue ETL

Answer

Amazon Kinesis Data Firehose

Question 3

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Analytics for Apache Flink. The pipeline reads from a Kinesis data stream and writes to a S3 bucket. The job must recover quickly from failures without reprocessing large amounts of data. Which TWO configurations should be used? (Choose TWO)

Accepted Answer

Enable checkpointing with a state backend like RocksDB.. Option A is correct because enabling checkpointing with a state backend like RocksDB allows Apache Flink to periodically save the state of the streaming application to durable storage. In the event of a failure, Flink can restart from the last completed checkpoint, avoiding the need to reprocess large amounts of data from the beginning of the stream. RocksDB is specifically designed for large state and provides fast recovery by storing state on disk with memory caching, making it ideal for production streaming pipelines.

Answer

Use in-memory state backend for low latency.

Answer

Set the parallelism to the maximum number of shards.

Answer

Increase the retention period of the Kinesis stream to 365 days.

Question 4

A data scientist is using SageMaker to train a model with a custom algorithm. The training script uses TensorFlow and runs on GPU instances. The training job fails with 'CUDA_ERROR_OUT_OF_MEMORY'. What is the most likely cause?

Accepted Answer

The batch size is too large for the GPU memory. The error 'CUDA_ERROR_OUT_OF_MEMORY' indicates that the GPU memory has been exhausted during training. In TensorFlow, the batch size directly determines how many samples are processed simultaneously on the GPU; a batch size that is too large will exceed the available GPU memory, causing this specific CUDA error. Reducing the batch size is the standard fix for this issue.

Answer

The S3 bucket is in a different region

Answer

The GPU driver is outdated

Answer

The training script has a memory leak on CPU

Answer

The instance type does not have enough CPU cores

Question 5

A data scientist is developing a deep learning model for object detection using Amazon SageMaker. The training dataset has 50,000 labeled images. The data scientist wants to improve model generalization without collecting more data. Which TWO techniques can be applied? (Choose two.)

Accepted Answer

Apply data augmentation techniques such as random cropping and horizontal flipping.. Option C is correct because data augmentation techniques like random cropping and horizontal flipping artificially expand the training dataset by generating modified versions of existing images. This exposes the model to more varied input patterns, reducing overfitting and improving generalization without requiring new labeled data.

Answer

Increase the learning rate to speed up convergence.

Answer

Increase the number of training epochs to ensure convergence.

Answer

Increase the batch size to reduce variance.

Question 6

A team is training a deep learning model on SageMaker using a custom Docker container. The training job fails with 'OutOfMemoryError'. The instance type is ml.p3.2xlarge with 61 GB memory. Which change should increase available memory?

Accepted Answer

Set the 'shm-size' parameter in the SageMaker training container to a larger value.. The 'OutOfMemoryError' in a SageMaker training container often stems from insufficient shared memory (/dev/shm) for data-loading workers, especially with PyTorch or TensorFlow dataloaders that use multiprocessing. Increasing the 'shm-size' parameter allocates more shared memory to the container, resolving the error without altering the model or instance type.

Answer

Reduce the batch size to use less memory.

Answer

Use SageMaker distributed data parallelism to distribute the model across multiple instances.

Answer

Mount an Amazon FSx for Lustre file system to offload data.

Question 7

A data scientist is using SageMaker to train a TensorFlow model. The training script uses tf.data.Dataset to load data from S3. Training is slow because of I/O bottleneck. Which change should the data scientist make to improve I/O performance?

Accepted Answer

Use Pipe input mode for the training channel.. Option B is correct because Pipe input mode streams data directly from S3 into the training algorithm without writing to disk, eliminating the I/O bottleneck caused by downloading entire files. This is particularly effective with tf.data.Dataset, as the pipeline can consume data incrementally, reducing latency and improving throughput for large datasets.

Answer

Enable EBS optimization on the training instance.

Answer

Use SageMaker local mode for training.

Answer

Convert the dataset to RecordIO format.

Question 8

A company wants to deploy a machine learning model that requires GPU acceleration for inference. The model is small and can fit on a single GPU. Which SageMaker endpoint configuration is MOST cost-effective?

Accepted Answer

Use a ml.p3.2xlarge instance with 1 GPU and enable automatic scaling.. Option D is the most cost-effective because it uses a single-GPU ml.p3.2xlarge instance, which matches the requirement that the model fits on one GPU, and enables automatic scaling to handle variable traffic without over-provisioning. This avoids paying for unused GPU capacity while still providing the necessary GPU acceleration for inference.

Answer

Use a ml.p3.16xlarge instance with 8 GPUs.

Answer

Use a SageMaker Serverless Inference endpoint.

Answer

Use a Multi-Model Endpoint on a ml.g4dn.xlarge instance.

Question 9

A machine learning engineer is training a model using Amazon SageMaker. The training data is stored in S3 and is 10 TB. The engineer wants to use Pipe input mode to stream data from S3. Which algorithm supports Pipe mode?

Accepted Answer

Amazon SageMaker Linear Learner. Amazon SageMaker's built-in algorithms, including Linear Learner, K-Means, XGBoost, and PCA, all support Pipe mode for streaming training data from S3. Therefore, all the listed options (A, B, C, D) are correct.

Question 10

A data scientist is training a neural network using a custom loss function. The training process converges, but the model's performance on the validation set is poor. The data scientist suspects that the model is overfitting. Which action should the data scientist take to diagnose overfitting?

Accepted Answer

Plot the training and validation loss over epochs. Plotting the training and validation loss over epochs is the standard diagnostic technique for detecting overfitting. If the training loss continues to decrease while the validation loss plateaus or increases, it indicates that the model is memorizing the training data rather than generalizing. This visual comparison directly confirms overfitting, allowing the data scientist to take corrective action such as regularization or early stopping.

Answer

Add more layers to the network

Answer

Increase the learning rate

Answer

Compute the confusion matrix on the training set

Question 11

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to the user. What is the likely issue?

Accepted Answer

The policy is missing s3:ListBucket permission. The correct answer is B because the policy allows s3:GetObject and s3:PutObject on the bucket, but SageMaker training jobs also require s3:ListBucket permission to read objects. Without it, access is denied. Option A is wrong because the policy includes sagemaker:CreateTrainingJob. Option C is wrong because the error is about creating a training job, not a model. Option D is wrong because the policy does allow s3:PutObject.

Answer

The policy does not allow sagemaker:CreateTrainingJob

Answer

The policy does not allow sagemaker:CreateModel

Answer

The policy does not allow s3:PutObject

Question 12

A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is most likely to reduce overfitting?

Accepted Answer

Reduce the maximum depth of each tree. Reducing the maximum depth of each tree limits the complexity of individual decision trees, preventing them from memorizing noise and specific patterns in the training data. This directly addresses overfitting by enforcing simpler, more generalized splits, which improves performance on unseen test data.

Answer

Increase the number of trees

Answer

Switch to a linear model

Answer

Increase the number of features considered at each split

Question 13

A company uses SageMaker to train a time-series forecasting model using Amazon Forecast. The dataset contains historical sales data for 10,000 products over 2 years. Which data format is required for the target time series?

Accepted Answer

A CSV file with columns: timestamp, target_value, item_id. Amazon Forecast requires the target time series data to be in a CSV format with specific columns: timestamp, target_value, and item_id. This structured format allows the service to correctly identify the time series for each product and the target metric to forecast. The CSV format is the standard input for Forecast's built-in algorithms and ensures compatibility with the dataset import process.

Answer

A single JSON file with nested arrays

Answer

A text file with one value per line

Answer

A Parquet file partitioned by date

Question 14

A company wants to deploy a machine learning model that predicts customer churn. The model must provide interpretable predictions to explain why a customer is likely to churn. Which algorithm is most appropriate?

Accepted Answer

Decision tree. Decision trees are inherently interpretable because they produce a clear, rule-based structure that shows exactly which features and thresholds lead to a churn prediction. This white-box nature allows stakeholders to trace the reasoning for each prediction, meeting the requirement for interpretability without needing post-hoc explanation methods.

Answer

Gradient boosting machine

Answer

Support vector machine (SVM)

Answer

Deep neural network

Question 15

Which THREE of the following are valid metrics for evaluating a regression model?

Accepted Answer

R-squared (R²). R-squared (R²) is a valid regression metric that measures the proportion of variance in the dependent variable explained by the independent variables. It ranges from 0 to 1, with higher values indicating better fit, and is commonly used alongside other error metrics to assess model performance.

Answer

F1 score

Answer

Accuracy

Question 16

A data engineer needs to schedule an AWS Glue ETL job to run every hour. Which service should they use for scheduling?

Accepted Answer

Amazon CloudWatch Events. Amazon CloudWatch Events (now part of Amazon EventBridge) can trigger an AWS Glue ETL job on a schedule using a cron or rate expression. This is the native, serverless scheduling service for running jobs at fixed intervals, such as every hour, without needing to manage any infrastructure.

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)

Answer

AWS Step Functions

Question 17

A data engineer needs to collect and analyze log data from multiple EC2 instances in real-time. The solution should be serverless and scalable. Which TWO AWS services should be used?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service that can capture, transform, and load streaming log data from EC2 instances into destinations like Amazon S3 or Amazon OpenSearch Service in near real-time, with no infrastructure to manage. It automatically scales to handle high-throughput data streams, making it ideal for real-time log analytics.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon S3

Question 18

A data scientist is training a binary classifier using imbalanced data. Which TWO techniques can help improve model performance on the minority class? (Choose two.)

Accepted Answer

Use the F1 score as the evaluation metric.. The F1 score is the harmonic mean of precision and recall, making it a robust evaluation metric for imbalanced datasets because it captures both false positives and false negatives. Unlike accuracy, which can be misleadingly high when the majority class dominates, the F1 score provides a balanced measure of model performance on the minority class.

Answer

Undersample the majority class randomly.

Answer

Use accuracy as the evaluation metric.

Answer

Apply L1 regularization to the model.

Question 19

A data scientist is using SageMaker to train a model. The training data is stored in an S3 bucket in a different AWS account. What is required to allow SageMaker to access the data?

Accepted Answer

Configure the SageMaker execution role with a policy that grants cross-account access to the S3 bucket.. Option A is correct because SageMaker uses an IAM execution role to access resources. To allow cross-account access to an S3 bucket, the SageMaker execution role must have an IAM policy that grants s3:GetObject and s3:ListBucket permissions for the bucket, and the S3 bucket policy must also grant cross-account access to that role. This is the standard AWS mechanism for cross-account resource access.

Answer

Set up VPC peering between the two accounts.

Answer

Create a SageMaker notebook instance in the same account as the S3 bucket.

Answer

Launch the training job from a SageMaker notebook in the account containing the S3 bucket.

Question 20

Which TWO actions are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Apply log transformation to reduce the impact of extreme values.. Option B is correct because applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.

Answer

Replace the mean with the median for numerical features.

Answer

Remove all outliers without further investigation.

Answer

Assume outliers are errors and delete them.