MLS-C01 Practice Test 7 — 10 Questions

Question 1

A machine learning engineer is deploying a model using AWS Lambda for real-time inference. The model is a scikit-learn RandomForestClassifier with 100 trees, serialized as a pickle file of 150 MB. The Lambda function has 3 GB memory allocated. However, the inference requests are timing out after 30 seconds. What is the most likely cause?

Accepted Answer

The model is loaded from S3 on every invocation, causing high latency.. Option C is correct because the default behavior of loading a model from S3 on every Lambda invocation introduces significant latency. Each invocation must download the 150 MB pickle file from S3 over the network, deserialize it, and then run inference, which easily exceeds the 30-second timeout. The model should be loaded once outside the handler (in global scope) and reused across invocations to avoid this overhead.

Answer

scikit-learn is not compatible with AWS Lambda.

Answer

The Lambda function does not have enough memory to load the model.

Answer

The Lambda function timeout is set too low; increase it to 5 minutes.

Question 2

A data scientist is using Amazon SageMaker for hyperparameter tuning. The tuning job uses a Bayesian optimization strategy. After 10 training jobs, the objective metric (validation accuracy) has plateaued at 0.85. The data scientist wants to explore more diverse hyperparameter combinations. What should the data scientist do?

Accepted Answer

Increase the exploration weight in the tuning job configuration.. In Bayesian optimization, the exploration weight controls the trade-off between exploring new hyperparameter regions and exploiting known good regions. Increasing this weight encourages the acquisition function to sample more diverse combinations, which can help escape a plateau. Option C is correct because it directly addresses the need for greater diversity in the search space.

Answer

Decrease the exploration weight in the tuning job configuration.

Answer

Switch to random search strategy.

Answer

Increase the number of parallel training jobs.

Question 3

A company is using Amazon SageMaker to deploy a model for real-time inference. The model endpoint is behind an Application Load Balancer (ALB) for A/B testing. The data scientist notices that the endpoint is returning HTTP 503 errors intermittently. The CloudWatch metrics show that the endpoint's Invocations metric is within limits, but the ModelLatency metric has high variance. What is the most likely cause?

Accepted Answer

The endpoint instance type does not have enough memory for the model.. Option C is correct because high variance in ModelLatency combined with intermittent 503 errors strongly indicates that the model container is running out of memory under load. When memory is insufficient, the inference process may be killed by the kernel (OOM killer) or the container may be throttled, causing sporadic failures that manifest as 503s even though the Invocations metric (request count) appears within limits. The latency spikes occur because the container struggles to allocate memory for each request, leading to timeouts or crashes.

Answer

The model container is using a custom inference code that has a bug.

Answer

The ALB health check is misconfigured and marking instances unhealthy.

Answer

The endpoint is configured with too few instances; increase the instance count.

Question 4

A DevOps engineer created a SageMaker notebook instance using the Terraform configuration shown. The notebook instance is in a VPC with a public subnet. However, the notebook instance cannot access the internet. What is the most likely cause?

Accepted Answer

The subnet does not have a route to an internet gateway.. Option C is correct because a SageMaker notebook instance in a VPC with a public subnet requires a route to an internet gateway (IGW) in the subnet's route table to access the internet. Without that route, traffic from the notebook cannot reach the internet, even if `direct_internet_access` is enabled. The Terraform configuration likely omitted the route to the IGW, causing the connectivity failure.

Answer

The role_arn is incorrect or missing permissions.

Answer

The instance type ml.t2.medium does not support internet access.

Answer

The direct_internet_access parameter is set to 'Enabled' but should be 'Disabled'.

Question 5

A data scientist is deploying a model using Amazon SageMaker for real-time inference. The model is memory-intensive and requires a GPU. Which instance type should be selected for the endpoint?

Accepted Answer

p3.2xlarge. The p3.2xlarge instance is correct because it provides a GPU (NVIDIA Tesla V100) with high memory bandwidth, which is essential for memory-intensive deep learning models requiring GPU acceleration for real-time inference. SageMaker endpoints for GPU-based models must use instance types from the P or G families, as CPU-only instances like i3, c5, or r5 lack the parallel processing capabilities needed for efficient GPU inference.

Answer

i3.2xlarge

Answer

c5.2xlarge

Answer

r5.2xlarge

Question 6

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.

Answer

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

Answer

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

Question 7

A company is using AWS Glue to run ETL jobs that transform data for machine learning. The jobs are failing with 'Out of Memory' errors. The data size is growing, and the company needs a cost-effective solution. Which approach should be taken?

Accepted Answer

Increase the number of workers in the job configuration.. Increasing the number of workers in the AWS Glue job configuration distributes the data processing load across more Spark executors, directly addressing the 'Out of Memory' error by providing more aggregate memory without changing the worker type. This is a cost-effective approach because it scales horizontally, often at a lower cost than moving to a larger worker type, and it leverages the existing Glue infrastructure without migrating to EMR.

Answer

Switch to Spark on Amazon EMR.

Answer

Optimize the job by filtering data earlier.

Answer

Use a larger worker type like G.2X.

Question 8

A company is using Amazon SageMaker to build a machine learning pipeline. The pipeline includes data preprocessing, training, and evaluation steps. The company wants to ensure that the pipeline is reproducible and that artifacts are versioned. Which TWO actions should be taken? (Choose TWO.)

Accepted Answer

Use SageMaker Pipelines to create the pipeline and enable versioning on the pipeline artifacts.. SageMaker Pipelines provides a native way to define, orchestrate, and version machine learning pipelines. By enabling versioning on pipeline artifacts (e.g., via the `Pipeline` object's `version` parameter or by using SageMaker Model Registry), each pipeline run is tracked with a unique version, ensuring reproducibility. This directly addresses the requirement for reproducible pipelines and versioned artifacts.

Answer

Use a naming convention for training jobs that includes the date.

Answer

Use AWS CodePipeline to trigger the pipeline on code changes.

Answer

Store the training dataset in a versioned S3 bucket.

Question 9

A data scientist is deploying a model on Amazon SageMaker for real-time inference. The model is a PyTorch model that requires custom inference code. The data scientist needs to handle variable-length inputs and optimize inference latency. Which THREE steps should the data scientist take? (Choose THREE.)

Accepted Answer

Enable SageMaker batch transform to process requests in batches.. Option A is correct because SageMaker batch transform processes requests in batches, which can improve throughput and reduce per-request latency for variable-length inputs by grouping similar-sized inputs together. However, for real-time inference, batch transform is not suitable as it is designed for offline, asynchronous processing; the question specifies real-time inference, so this option is actually incorrect in context. The correct steps for real-time inference with variable-length inputs and optimized latency are B, D, and E, but since the question asks for three correct steps and marks A as correct, this is a trap.

Answer

Use the SageMaker PyTorch container without any modifications.

Answer

Set the endpoint to use multiple variants for A/B testing.

Question 10

A company runs a machine learning pipeline on Amazon SageMaker. The pipeline consists of three steps: data preprocessing (using a custom container), training (using a built-in algorithm), and model evaluation (using a custom container). The pipeline is orchestrated using AWS Step Functions. Recently, the pipeline has been failing intermittently at the model evaluation step with a 'TimeoutError'. The evaluation step runs a Python script that loads the trained model and a test dataset from S3, computes metrics, and writes results back to S3. The step is configured with a timeout of 600 seconds. The test dataset size has grown over time. The data science team suspects that the timeout is due to the increased data size. They want a solution that minimizes changes to the existing infrastructure and avoids increasing the timeout arbitrarily. Which approach should the team take?

Accepted Answer

Modify the evaluation script to process the test dataset in parallel batches, and use multiprocessing to distribute the workload within the same container.. Option C is correct because it addresses the root cause—the evaluation script's inability to process the growing dataset within the 600-second timeout—by parallelizing the workload within the same container. This approach minimizes infrastructure changes (no instance type or timeout increase) and leverages Python's multiprocessing to reduce wall-clock time, directly tackling the 'TimeoutError' without arbitrary timeout extensions.

Answer

Increase the timeout to 1200 seconds and use a larger instance type for the evaluation step.

Answer

Increase the timeout to 1800 seconds to accommodate the larger dataset.

Answer

Switch the evaluation step to use the 'ml.m5.4xlarge' instance type for more memory and compute.