MLS-C01 Machine Learning Implementation and Operations Practice Test 13 — 15 Questions

Question 1

A machine learning engineer needs to deploy a model that requires custom inference code with dependencies. Which SageMaker deployment option should be used?

Accepted Answer

Create a custom Docker container and deploy to SageMaker endpoint.. When a model requires custom inference code with dependencies, the only way to fully control the runtime environment, libraries, and inference logic is to package everything into a custom Docker container. SageMaker endpoints can then deploy this container, allowing the engineer to specify the exact inference script and dependencies (e.g., via a Dockerfile and a requirements.txt). This approach supports any framework or custom logic that built-in algorithms cannot provide.

Answer

Use a SageMaker notebook instance as an endpoint.

Answer

Use a built-in SageMaker algorithm.

Answer

Use a SageMaker batch transform job.

Question 2

A company is training a deep learning model on SageMaker using multiple GPUs. The training is slow due to inefficient data loading. Which TWO actions can improve I/O performance?

Accepted Answer

Use Pipe mode input for training data.. Pipe mode streams training data directly from Amazon S3 to the training algorithm, bypassing the need to download data to disk before training begins. This eliminates disk I/O bottlenecks and reduces data loading latency, which is critical for GPU-intensive training where GPUs may otherwise idle waiting for data.

Answer

Use instance store volumes for data.

Answer

Increase the instance count to a single large instance.

Answer

Use Amazon EBS volumes attached to training instances.

Question 3

A data scientist is using SageMaker to build a model for fraud detection. The dataset is highly imbalanced. Which THREE techniques should be applied to address class imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples of the minority class.. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the dataset without simply duplicating data. This is effective for fraud detection because it provides the model with more diverse examples of fraudulent transactions, reducing the bias toward the majority class.

Answer

Train the model only on the majority class.

Answer

Use accuracy as the evaluation metric.

Question 4

A company is using SageMaker Autopilot to automatically build ML models. They want to ensure that the generated models are reproducible. Which TWO settings should they configure?

Accepted Answer

Set a random seed.. Setting a random seed (Option A) ensures that the stochastic processes in model training (e.g., weight initialization, data shuffling, and hyperparameter sampling) produce identical results across runs. SageMaker Autopilot uses algorithms like XGBoost and linear learners that rely on randomness; fixing the seed guarantees reproducibility of the final model.

Answer

Use multiple trials.

Answer

Enable early stopping.

Answer

Enable automatic feature engineering.

Question 5

A company operates a real-time fraud detection system using SageMaker. The model is deployed on an ml.c5.xlarge instance behind an Application Load Balancer (ALB). Recently, during a sales event, traffic spiked and the endpoint returned HTTP 503 errors. The team scaled the instance count from 2 to 5, but errors persisted. CloudWatch metrics show low CPU utilization (~30%) and high memory usage (~90%). The model loads a large dictionary file (2GB) into memory at startup. Which action should resolve the issue?

Accepted Answer

Switch to a compute-optimized instance type like c5.2xlarge.. The high memory usage (~90%) with low CPU (~30%) indicates the instance is memory-constrained under load. The ml.c5.xlarge has 8 GB memory, and the model's 2 GB dictionary plus overhead exhausts memory, causing 503 errors. Scaling out doesn't help because each instance is individually memory-bound. Option B switches to c5.2xlarge, which provides 16 GB memory (doubling capacity) and more CPU cores, addressing both memory exhaustion and ensuring sufficient compute for concurrent requests. Option A (Spot instances) does not increase per-instance memory. Option C (scaling to 10 instances) still uses c5.xlarge instances with 8 GB each, so each remains memory-limited. Option D (r5.large) offers 16 GB memory but reduces vCPUs to 2, which could create a CPU bottleneck for the inference workload, making B the better choice.

Answer

Enable auto-scaling with Spot instances.

Answer

Increase the number of instances further to 10.

Answer

Use a memory-optimized instance type like r5.large.

Question 6

A research lab is using SageMaker to train deep learning models on a custom dataset stored in S3. Each training job uses a single ml.p3.2xlarge instance. Recently, training jobs have been failing intermittently with 'NetworkError: Connection reset by peer' during the data download phase. The data scientist notices that the dataset is 50GB and the network throughput is low. The training script uses the default S3 download method (boto3) to copy data from S3 to the local instance storage. Which solution should the data scientist implement to resolve the issue?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3.. SageMaker Pipe mode streams training data directly from S3 without writing to local disk, avoiding the large download that causes network timeouts. Option A (mount EBS) does not eliminate the need to download 50GB, so it does not resolve the network reset. Option C (retry logic) may help but does not address the root cause of low throughput and large downloads. Option D (larger instance) increases bandwidth but does not guarantee connection stability and costs more.

Answer

Mount an EBS volume to the instance and copy data there before training.

Answer

Add retry logic in the training script to handle network errors.

Answer

Use a larger instance type like p3.8xlarge for better network bandwidth.

Question 7

A media company uses SageMaker to train a recommendation model. The training data is stored in an S3 bucket with versioning enabled. The data pipeline updates the training data daily by overwriting objects with new data. Recently, the model's performance degraded, and the team suspects that the training data was corrupted on a specific day. They want to train the model using the data from a previous version. How can the team retrieve the previous version of the training data?

Accepted Answer

Use the S3 GET Object Version API to download the specific version of each object.. S3 versioning allows retrieval of any previous version of an object by using the GET Object Version API with the specific version ID. Option A is incorrect because S3 Glacier is an archival storage class, not a feature for accessing previous versions of current objects. Option C is incorrect because S3 Select is used to query data within an object, not for version retrieval. Option D is incorrect because S3 replication copies objects to another bucket but does not provide access to previous versions in the source bucket.

Answer

Restore the bucket from S3 Glacier, which contains the previous version.

Answer

Use S3 Select to query the previous version of the data.

Answer

Enable S3 replication to a different bucket and use the replicated data.

Question 8

A machine learning team is using Amazon SageMaker to train a deep learning model on a large dataset stored in Amazon S3. The training job is taking too long. The team wants to reduce training time without modifying the model architecture. Which THREE actions should the team take? (Choose 3.)

Accepted Answer

Enable SageMaker Managed Spot Training to use cheaper spot instances.. SageMaker Managed Spot Training enables the use of spot instances at a reduced cost, allowing the team to allocate more resources (e.g., more or larger instances) within the same budget, which can directly reduce training time. Option C is correct because distributed training across multiple GPU instances parallelizes the workload, significantly reducing training duration. Option D is correct because Pipe input mode streams data from Amazon S3 directly to the training algorithm, minimizing I/O bottlenecks and reducing time spent waiting for data to load. Option B is not one of the three best choices because simply using a larger instance may not fully address I/O or parallelism bottlenecks and can be more expensive. Option E is incorrect because SageMaker Processing is designed for data preprocessing, not for accelerating training itself.

Answer

Use a larger instance type with more vCPUs and memory.

Answer

Use SageMaker Processing to preprocess the data.

Question 9

A machine learning engineer is deploying a custom XGBoost model for real-time inference on Amazon SageMaker. The model was trained using the SageMaker XGBoost built-in algorithm. The endpoint is deployed with an ml.m5.large instance and is receiving around 50 requests per second. The engineer notices that the endpoint's latency is around 200 ms, but the requirement is under 100 ms. The model's serialized format is a .tar.gz file. The engineer wants to reduce inference latency without modifying the model or retraining. What should the engineer do?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance.. SageMaker Neo compiles trained models to optimize them for target hardware, reducing inference latency without modifying the model. Option A is wrong because SageMaker Debugger is used for monitoring training jobs and debugging, not for optimizing inference code. Option B is wrong because SageMaker Elastic Inference attaches GPU acceleration, which is beneficial for deep learning models but not for XGBoost (a tree-based model). Option D is wrong because SageMaker Batch Transform is designed for batch predictions on large datasets, not for real-time inference with low latency requirements.

Answer

Configure SageMaker Debugger to optimize the inference code.

Answer

Use SageMaker Elastic Inference to attach an accelerator.

Answer

Use SageMaker Batch Transform instead of a real-time endpoint.

Question 10

A machine learning team is using Amazon SageMaker to train a PyTorch model on a dataset that is 500 GB in size. The training job runs on a single ml.p3.2xlarge instance, but the training takes over 48 hours, which exceeds the maximum allowed time. The team wants to reduce training time to under 24 hours. They are open to using multiple instances and have budget for up to 4 instances. The dataset is stored in Amazon S3 and can be split into shards by a key. The model architecture must remain unchanged. What should the team do?

Accepted Answer

Use SageMaker distributed data parallelism with 4 ml.p3.2xlarge instances.. SageMaker's distributed data parallelism library (SMDDP) efficiently splits the dataset across multiple GPUs, allowing the training to complete in approximately 1/4 of the time (assuming near-linear scaling). This directly addresses the timeout issue. Option B is incorrect: SageMaker Processing is for data preprocessing, not model training; training separate models would not produce a single model. Option C is incorrect: upgrading to a single larger instance (ml.p3.16xlarge) may provide up to 8x more GPU power but may not reduce training time to under 24 hours due to memory and I/O bottlenecks, and it exceeds the budget limitation of using up to 4 instances. Option D is incorrect: Pipe input mode reduces data loading latency but does not reduce the computation required for training, so it would not sufficiently decrease training time.

Answer

Use SageMaker Processing to split the data and train separate models.

Answer

Change the instance type to ml.p3.16xlarge.

Answer

Switch to Pipe input mode to stream data faster.

Question 11

A company is using Amazon SageMaker to host a model for real-time inference. The model was trained using SageMaker's built-in Linear Learner algorithm. The endpoint has been running for a week, and the operations team notices that the endpoint's latency has increased from 50 ms to 150 ms over the past few days. The number of requests per second has remained steady at about 200. The team suspects a memory leak in the inference container. What should the team do to diagnose the issue?

Accepted Answer

Enable CloudWatch Logs and use Container Insights to view memory utilization.. CloudWatch Container Insights provides metrics for containerized applications, including memory utilization. This can help diagnose a memory leak by showing memory usage trends over time. Option B is incorrect because CloudWatch latency metrics only show endpoint response time, not memory usage. Option C is incorrect because SageMaker Debugger is designed for debugging training jobs, not inference containers. Option D is incorrect because SageMaker Model Monitor detects data and model drift, not memory leaks.

Answer

Use Amazon CloudWatch to monitor the endpoint's latency metric.

Answer

Use SageMaker Debugger to inspect the inference container.

Answer

Use SageMaker Model Monitor to detect data drift.

Question 12

A data scientist is using Amazon SageMaker to train a TensorFlow model on a dataset that includes sensitive personal information (PII). The data is stored in Amazon S3 with server-side encryption using AWS KMS (SSE-KMS). The training job fails with an Access Denied error when trying to read from S3. The data scientist has already verified that the SageMaker execution role has s3:GetObject permissions on the S3 bucket. What additional configuration is needed?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role.. When S3 objects are encrypted with SSE-KMS, the SageMaker execution role must have the kms:Decrypt permission to decrypt the data during training. Even though the role has s3:GetObject access, the KMS key policy or the role's IAM policy must explicitly allow decryption of the KMS key used for server-side encryption. Without this, SageMaker cannot read the encrypted objects, resulting in an Access Denied error.

Answer

Add kms:Encrypt permission to the SageMaker execution role.

Answer

Add a bucket policy that grants s3:GetObject to the SageMaker role.

Answer

Configure a VPC endpoint for S3 and attach a policy.

Question 13

A company is using Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.c5.xlarge instance. The company wants to reduce costs without affecting performance. The current traffic pattern shows a daily peak of 500 requests per second for 2 hours, and the rest of the day sees fewer than 50 requests per second. The model has a cold start time of about 30 seconds. What should the company do?

Accepted Answer

Configure an auto scaling policy that scales down during low traffic and keep a minimum of 1 instance.. Configuring an auto scaling policy that scales down during low traffic reduces costs, and keeping a minimum of 1 instance avoids cold starts during low traffic, ensuring low latency. Option A is incorrect because serverless endpoints have cold starts and may not handle the peak of 500 TPS. Option C is wrong because a single instance may not handle the peak traffic, causing latency. Option D is wrong because Batch Transform is for batch predictions, not real-time inference.

Answer

Switch to a serverless inference endpoint.

Answer

Use a single ml.c5.xlarge instance and rely on it.

Answer

Use SageMaker Batch Transform for all predictions.

Question 14

A machine learning engineer is deploying a model on Amazon SageMaker that was trained using a custom Docker container. The container is stored in Amazon ECR. The engineer creates a SageMaker model and endpoint configuration, but when creating the endpoint, it fails with an error: 'Could not find the inference code at the expected path.' The engineer verified that the container image is correct and the model artifacts are in S3. What is the most likely cause?

Accepted Answer

The inference code is not placed in the /opt/ml/model directory inside the container.. The error 'Could not find the inference code at the expected path' indicates that SageMaker cannot locate the inference script (e.g., serve.py, inference.py) inside the container. SageMaker expects the inference code to be placed in the /opt/ml/model/ directory within the container. Option D correctly identifies that the inference code is not placed in /opt/ml/model. Option A is incorrect because container compatibility would cause a different error (e.g., unsupported base image). Option B is incorrect because ECR pull permissions typically result in an 'Unauthorized' error when pulling the image. Option C is incorrect because model artifacts being in the wrong format would cause loading errors, not inference code path errors.

Answer

The container is not compatible with the SageMaker inference environment.

Answer

The SageMaker execution role does not have ECR pull permissions.

Answer

The model artifacts are not in the correct format.

Question 15

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job uses a hyperparameter tuning job to optimize hyperparameters. The tuning job has been running for 3 hours and has completed 20 training jobs. The data scientist wants to stop the tuning job early if it is not making progress. What should the data scientist do to accomplish this?

Accepted Answer

Configure the tuning job with early stopping enabled.. SageMaker's automatic model tuning supports early stopping. When enabled, the tuning job stops automatically if no significant improvement is observed. Option B is incorrect because CloudWatch alarms can monitor metrics but cannot directly stop a tuning job; they can trigger actions like notifications but not stop the tuning. Option C is incorrect because SageMaker Experiments is for tracking experiments, not for stopping tuning jobs. Option D is incorrect because SageMaker Debugger stops individual training jobs, not the hyperparameter tuning job itself; early stopping is a built-in feature of the tuning job.

Answer

Set up a CloudWatch alarm to stop the tuning job if a metric does not improve.

Answer

Use SageMaker Experiments to monitor and manually stop the tuning job.

Answer

Use SageMaker Debugger to stop training jobs that are not improving.