MLS-C01 Mock Exam 90 — 25 Questions

Question 1

A data scientist is using Amazon SageMaker to train a deep learning model. The training job is taking too long. Which THREE actions can reduce training time?

Accepted Answer

Use incremental training to continue from a previous model. Incremental training allows you to start from a previously trained model, which reduces training time because the model does not need to learn from scratch. SageMaker's incremental training loads the existing model artifacts and continues training on new data, significantly cutting down the time required to converge compared to full retraining.

Answer

Use Spot Instances to reduce cost

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Decrease the batch size to reduce memory usage

Question 2

A data scientist is using Amazon SageMaker to train a deep learning model. The training job is taking too long. Which THREE actions can reduce training time?

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

Accepted Answer

The number of training steps or epochs specified exceeds the dataset size.. The 'OutOfRangeError: End of sequence' error in TensorFlow occurs when the training loop attempts to read more data than is available in the dataset. This typically happens when the number of training steps or epochs specified exceeds the total number of records in the TFRecord files, causing the iterator to reach the end of the dataset prematurely.

Answer

The TFRecord files are corrupted.

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The instance type does not have enough memory.

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The shuffle buffer size is too large.

Question 3

A company wants to automate the retraining of a model weekly using new data. The training script is in a SageMaker notebook. Which implementation is most maintainable?

Accepted Answer

Convert the notebook to a Python script, create a Docker container, and use SageMaker Pipelines with a schedule. It transforms the notebook into a production-grade, containerized training pipeline that can be scheduled natively via SageMaker Pipelines. This approach decouples the training logic from the notebook environment, ensures reproducibility through Docker, and leverages SageMaker's managed infrastructure for automated retraining without manual intervention.

Answer

Set up a cron job on an EC2 instance to run the training script

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Schedule the notebook to run via a SageMaker Lifecycle Configuration script

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Use AWS CloudFormation to provision a training job on a schedule

Question 4

A data scientist is trying to create a training job named 'test-model' using an IAM role with the attached policy. The creation fails with an AccessDenied error. What is the most likely cause?

Accepted Answer

The Deny statement uses a wildcard '*' in the condition value, which is not supported for StringNotEquals.. The Deny statement uses 'StringNotEquals' with a wildcard '*' in the condition value, which is not supported for the 'StringNotEquals' condition operator in IAM policies. The 'StringNotEquals' operator requires exact string matching and does not support wildcards; using '*' will cause the condition to never match, effectively making the Deny statement non-functional or causing unexpected behavior. This mismatch leads to an AccessDenied error because the policy evaluation fails to properly deny or allow the action.

Answer

The Resource is set to '*' and should be specific.

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The Deny statement uses 'StringNotEquals' which should be 'StringEquals'.

Answer

The IAM role does not have permission to assume the SageMaker execution role.

Question 5

A company runs a daily batch ETL job using AWS Glue that reads from Amazon RDS (MySQL), transforms the data, and writes to Amazon Redshift. The job takes 6 hours and processes 500 GB of data. Management wants to reduce the runtime. Which action would be MOST effective?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job directly increases the parallelism of the Spark-based ETL job, allowing it to process the 500 GB of data faster. Since the bottleneck is the Glue job's compute capacity, adding more DPUs reduces runtime without changing the source or target infrastructure.

Answer

Increase the node size of the Redshift cluster

Answer

Use the Redshift COPY command to load data directly from RDS

Answer

Use Amazon RDS with Provisioned IOPS SSD storage

Question 6

A team is deploying a SageMaker endpoint for a model that was trained with scikit-learn. The endpoint receives spikes in traffic during business hours. The team wants to minimize cost while ensuring availability during spikes. Which endpoint configuration is MOST appropriate?

Accepted Answer

Use a production variant endpoint with auto-scaling based on CPU utilization. A production variant endpoint with auto-scaling based on CPU utilization allows the SageMaker endpoint to dynamically adjust the number of instances in response to traffic spikes, ensuring availability during business hours while minimizing cost by scaling down during off-peak periods. This approach is ideal for a scikit-learn model, which is CPU-bound, making CPU utilization a relevant and effective scaling metric.

Answer

Use SageMaker Serverless Inference

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Use a multi-model endpoint with a single instance type

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Deploy a single large instance that can handle peak load

Question 7

A company is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a GPU for low-latency predictions. Which instance type should be chosen?

Accepted Answer

ml.g4dn.xlarge. The ml.g4dn.xlarge instance is correct because it includes an NVIDIA T4 GPU, which is required for low-latency real-time inference with deep learning models. GPU instances accelerate matrix operations and parallel processing, reducing inference latency compared to CPU-only instances.

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ml.c5.xlarge

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ml.r5.2xlarge

Answer

ml.m5.large

Question 8

A data engineering team is migrating on-premises Hadoop workloads to AWS. The workloads include batch processing using Apache Spark and interactive SQL queries. The data is stored in HDFS. Which TWO AWS services should be used to replace HDFS and provide a scalable, durable storage layer? (Choose TWO.)

Accepted Answer

Amazon EMR with EMRFS. Amazon S3 provides a highly durable (99.999999999% durability), scalable, and cost-effective object storage layer that replaces HDFS for Hadoop workloads. Amazon EMR with EMRFS allows Spark and Hive to read and write data directly from S3, treating it as a native filesystem with features like consistent view and read-after-write consistency, making it the ideal compute layer for batch processing and interactive SQL queries.

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Amazon EBS

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Amazon FSx for Lustre

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Amazon RDS

Question 9

A data scientist is training a neural network for a multi-class classification problem with 100 classes. The model uses a softmax output layer and cross-entropy loss. During training, the loss decreases steadily but the accuracy on the validation set plateaus early. Which of the following is the most likely cause?

Accepted Answer

The model is overfitting the training data. When the validation accuracy plateaus early while training loss continues to decrease, it indicates that the model is memorizing the training data rather than learning generalizable patterns. This is classic overfitting, where the softmax output layer produces high-confidence predictions for training samples but fails to generalize to unseen validation data, causing cross-entropy loss to drop on the training set while validation accuracy stagnates.

Answer

Batch size is too large

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Number of epochs is too small

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Learning rate is too high

Question 10

A company is using SageMaker to deploy a model for real-time inference. The model requires low latency, and the company wants to test the endpoint before production. Which approach should be used to validate endpoint performance?

Accepted Answer

Use SageMaker's built-in shadow testing or load testing features.. SageMaker provides features like shadow testing, which allows you to test a new variant alongside the existing production variant without impacting live traffic, and integration with load testing tools to simulate traffic and measure latency before full production deployment. Option A is incorrect because CloudWatch Synthetics is used for monitoring endpoint health and availability, not for pre-production load or performance testing. Option B is incorrect because offline batch evaluation assesses model accuracy on a static dataset, but does not test real-time inference performance metrics such as latency and throughput. Option C is incorrect because deploying directly to production and monitoring exposes users to potential performance issues; pre-production validation should be conducted first.

Answer

Use CloudWatch Synthetics to create a canary.

Answer

Perform offline batch evaluation on a test dataset.

Answer

Deploy to production and monitor using CloudWatch.

Question 11

A data scientist is granted the IAM policy shown in the exhibit. The data scientist can query the 'data-lake-bucket' using Athena and get results. However, when the data scientist tries to run a CTAS (CREATE TABLE AS SELECT) query in Athena to write results to a new S3 location, the query fails. What is the most likely reason?

Accepted Answer

The policy does not grant s3:PutObject permission on the bucket.. The policy allows s3:GetObject and s3:ListBucket, but not s3:PutObject, which is required for CTAS queries. Option A is wrong because the policy uses resource-level permissions for S3. Option C is wrong because Athena does not require Glue Data Catalog permissions for CTAS if the table metadata is already stored. Option D is wrong because the policy does not restrict Athena resource ARNs.

Answer

The policy does not grant athena:CreateTable permission.

Answer

The policy does not grant permissions to the Glue Data Catalog.

Answer

The policy uses a wildcard for Athena actions, which is not allowed.

Question 12

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 100 Mbps connection to AWS. The transfer must be completed within one week. Which approach should the engineer use?

Accepted Answer

Use AWS Snowball Edge device to physically transfer the data.. The on-premises network has a 100 Mbps connection, which yields a theoretical maximum transfer of about 1.08 TB per day (100 Mbps * 86400 seconds / 8 bits per byte / 1024^4 bytes per TB). To transfer 50 TB within 7 days, the required throughput is approximately 7.14 TB per day, far exceeding the available bandwidth. AWS Snowball Edge provides a physical shipping method that bypasses network constraints entirely, making it the only viable option for this volume and timeline.

Answer

Use Amazon S3 Transfer Acceleration.

Answer

Use AWS DataSync to transfer the data over the network.

Answer

Use multiple concurrent AWS CLI copy commands over VPN.

Question 13

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

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Amazon Simple Queue Service (SQS)

Answer

AWS Step Functions

Question 14

A data scientist is training a binary classification model on a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.

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Precision

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Root Mean Squared Error (RMSE)

Answer

Accuracy

Question 15

A data engineering team is designing a data lake on Amazon S3. The data is ingested from multiple sources in JSON, CSV, and Parquet formats. The team needs to make the data available for analysis using Amazon Athena and Amazon Redshift Spectrum. The team wants to minimize data transformation costs and storage overhead. Which data storage approach should the team use?

Accepted Answer

Convert all data to Apache Parquet before storing in S3. Converting all data to Apache Parquet before storing in S3 minimizes storage overhead and improves query performance. Parquet is a columnar format that provides efficient compression and encoding schemes, reducing storage costs. It is natively supported by Amazon Athena and Redshift Spectrum, enabling fast analytics without on-the-fly conversion. Option B (storing in original format) increases storage costs and can degrade query performance, especially with JSON or CSV. Option C incurs transformation costs each time data is queried, negating any storage benefit. Option A adds unnecessary transformation steps and cluster costs. Therefore, upfront conversion to Parquet is the most cost-effective strategy for this use case.

Answer

Load the data into Amazon Redshift cluster and then unload to S3 in Parquet

Answer

Store the data in its original format in S3 and use Athena to query directly

Answer

Store the data in its original format and use AWS Glue to convert to Parquet when queried

Question 16

A data scientist is setting up a SageMaker training job and has attached this IAM policy to the execution role. The training job fails with an access denied error when trying to write to the output path 's3://my-bucket/output/model.tar.gz'. What additional permission is needed?

Accepted Answer

s3:ListBucket. The training job fails because SageMaker needs to verify that the output S3 bucket exists before writing to it. The s3:ListBucket permission is required to list the contents of the bucket (or confirm its existence) as part of the write operation. Without this permission, the service cannot validate the bucket, resulting in an access denied error even if s3:PutObject is allowed.

Answer

s3:GetObject for the output path

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s3:DeleteObject

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iam:PassRole on the role itself

Question 17

A company has a real-time inference endpoint on Amazon SageMaker that uses a custom container. The endpoint is experiencing high latency and occasional 502 errors. The logs from the container show that the model inference time is low, but the overall response time is high. Which step is MOST likely to reduce the latency?

Accepted Answer

Increase the number of instances and enable auto-scaling. Increasing the number of instances and enabling auto-scaling helps distribute the incoming request load, reducing queuing delays at the endpoint. The logs show inference time is low, so the high latency is likely due to request queuing behind other requests. Scaling out addresses this. Option A is wrong because batch transform is designed for offline, batch processing, not real-time inference. Option B is wrong because using a larger instance type may provide more compute but does not directly address queuing; it is also less cost-effective than scaling out. Option C is wrong because the model inference time is already low, so further optimization would have minimal impact on overall latency.

Answer

Switch to batch transform to process requests in batches

Answer

Use a larger instance type for the endpoint

Answer

Optimize the model to reduce inference time

Question 18

A machine learning engineer is deploying a model to Amazon SageMaker for real-time inference. The model requires low latency and must handle variable traffic patterns. Which SageMaker feature should the engineer use to automatically scale the number of instances based on demand?

Accepted Answer

SageMaker automatic scaling. SageMaker automatic scaling (Application Auto Scaling) is the correct feature because it allows the engineer to define scaling policies (e.g., based on CPU utilization or request latency) that automatically adjust the number of instances behind a SageMaker endpoint in response to real-time traffic patterns. This ensures low latency by maintaining sufficient capacity during spikes and reducing costs during lulls, without manual intervention.

Answer

Amazon EC2 Auto Scaling

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Elastic Inference

Answer

SageMaker Batch Transform

Question 19

A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (Parquet format, 10 TB). The data scientists have been running training jobs using the File mode input, but the jobs are taking too long due to data download time. They want to reduce the training start-up time and overall training time. Which solution is MOST cost-effective and efficient?

Accepted Answer

Configure the SageMaker training job to use Pipe mode, which streams data directly from S3 without downloading to the instance's local storage.. Pipe mode in SageMaker streams training data directly from Amazon S3 to the training algorithm without first downloading it to the instance's local storage. This eliminates the data download step, significantly reducing startup time and overall training time for large datasets like 10 TB. It is the most cost-effective because it avoids the need for larger instances or additional data transfer acceleration services.

Answer

Use S3 Transfer Acceleration to speed up the data transfer from S3 to the training instance.

Answer

Use larger EC2 instances with more vCPUs and memory to speed up the training process.

Answer

Enable Elastic Fabric Adapter (EFA) on the training instances to improve network throughput.

Question 20

You are deploying a custom Docker container for a SageMaker model that requires a specific NVIDIA CUDA version. Which THREE steps must you take to ensure the container runs correctly on SageMaker?

Accepted Answer

Include the SageMaker inference toolkit in the container. The SageMaker inference toolkit provides the necessary SageMaker-compatible HTTP server and lifecycle management (e.g., model loading, serving, and health checks) that SageMaker expects from a custom container. Without it, the container would not properly integrate with SageMaker's invocation and scaling mechanisms, even if the CUDA dependencies are correct.

Answer

Define a health check endpoint

Answer

Use SageMaker Batch Transform