MLA-C01 Practice Test 15 — 15 Questions

Question 1

A financial services company is developing a fraud detection model using Amazon SageMaker. They have a dataset with 10 million transactions, each with 300 features. The dataset is highly imbalanced (0.1% fraud). They have performed feature engineering and now need to split the data for training, validation, and test sets. The data is stored in CSV files in Amazon S3. They plan to use SageMaker's built-in XGBoost algorithm. To ensure proper evaluation and avoid data leakage, which data splitting strategy should they use?

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

Accepted Answer

Perform a stratified split on the target variable to ensure each set has the same fraud ratio.. A stratified split preserves the original 0.1% fraud ratio across training, validation, and test sets, which is critical for imbalanced datasets. This ensures each subset is representative of the population, allowing SageMaker's XGBoost to be evaluated fairly without data leakage. Random splits (Option A) could accidentally create a validation or test set with zero fraud cases, making evaluation meaningless.

Answer

Randomly shuffle the entire dataset and then split into 80% training, 10% validation, 10% test.

Answer

Use k-fold cross-validation on the entire dataset and average the results.

Answer

Apply SMOTE to balance the dataset first, then split randomly into training, validation, and test sets.

Question 2

A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?

Accepted Answer

Log transformation (natural log). A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.

Answer

Standardization (z-score)

Answer

Min-max scaling to [0,1]

Answer

Equal-width binning

Question 3

A data scientist notices that a SageMaker endpoint is returning HTTP 5XX errors under high load. The endpoint uses a single ml.m5.large instance. The team wants to reduce these errors without changing the instance type. What is the most cost-effective step?

Accepted Answer

Configure auto-scaling for the endpoint with a target tracking policy. Configuring auto-scaling with a target tracking policy allows the endpoint to dynamically add more instances under high load, distributing the traffic and reducing HTTP 5XX errors. Since the team cannot change the instance type, scaling out is the most cost-effective way to handle increased demand, as it only adds capacity when needed and avoids over-provisioning.

Answer

Increase the endpoint's invocation timeout to 120 seconds

Answer

Deploy the model on a SageMaker batch transform job

Answer

Create a new endpoint with multiple instances and use weighted routing

Question 4

A team uses AWS Auto Scaling for a SageMaker real-time endpoint. They notice that when scaling in, the latest instance is always terminated first, causing disruption to recent requests. How can they configure the scaling policy to terminate the oldest instance first?

Accepted Answer

Configure the termination policy as 'OldestInstance'. AWS Auto Scaling for SageMaker endpoints supports a termination policy of 'OldestInstance', which explicitly instructs the scaling process to terminate the instance that has been running the longest. By default, Auto Scaling terminates the newest instance (the default termination policy), which can disrupt recent requests. Configuring the termination policy to 'OldestInstance' ensures that the oldest, most stable instance is removed first, minimizing disruption to in-flight requests.

Answer

No action needed; this is the default behavior

Answer

Use lifecycle hooks

Answer

Use AWS CloudFormation to manage the endpoint

Question 5

A team is deploying a model that requires GPU acceleration for inference. They are using an Amazon SageMaker real-time endpoint. The model is a large language model (LLM) that does not fit on a single GPU. Which configuration should they use to minimize latency while fitting the model?

Accepted Answer

Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.. SageMaker's model parallelism library allows you to shard a large language model across multiple GPUs within a single instance, enabling inference for models that exceed a single GPU's memory. This approach minimizes latency by keeping all GPUs in a single instance with high-speed interconnects (e.g., NVLink), avoiding the network overhead of distributing across separate instances.

Answer

Use data parallelism with Horovod to distribute inference across GPUs.

Answer

Optimize the model with SageMaker Neo to reduce its size.

Answer

Deploy the model across multiple endpoints and use a load balancer.

Question 6

A team wants to apply a custom container for inference on SageMaker. The container needs to implement a web server that responds to API requests. Which protocol and port must the container listen on to be compatible with SageMaker hosting?

Accepted Answer

The container must listen on port 8080 and use HTTP protocol.. SageMaker requires custom inference containers to listen on port 8080 and communicate over HTTP (not HTTPS). The SageMaker hosting service uses a proxy that terminates HTTPS and forwards plain HTTP requests to the container on port 8080. This ensures compatibility with the built-in model serving infrastructure.

Answer

The container must listen on port 8080 and use HTTPS protocol.

Answer

The container can listen on any port as long as the port is specified in the endpoint configuration.

Answer

The container must listen on port 8000 and use HTTP protocol.

Question 7

A startup wants to deploy a containerized ML application that includes both a model inference server and a preprocessing component in the same endpoint. Which SageMaker endpoint type supports running multiple containers?

Accepted Answer

Multi-container endpoint. SageMaker multi-container endpoints allow you to run up to 15 containers on a single endpoint, with containers invoked in a defined sequence (inference pipeline) or directly. This is the correct choice when a preprocessing component and a model inference server must coexist in one endpoint, since the preprocessing container can transform the request before passing it to the inference container. This pattern is ideal for encapsulating feature engineering with the model.

Answer

Asynchronous Inference

Answer

Multi-model endpoint

Answer

Real-time endpoint

Question 8

A team uses SageMaker Pipelines to automate model retraining. After a successful pipeline run, they want to register the new model version in the SageMaker Model Registry so that it can be reviewed for approval. Which step type should they add to the pipeline?

Accepted Answer

RegisterModelStep. The correct step type is `RegisterModelStep`, which is specifically designed to create a new model version in the SageMaker Model Registry after a training or processing step completes. This step captures the model artifacts, training metrics, and metadata, and registers them under a specified model package group for approval workflows. Other step types serve different pipeline functions and do not interact with the Model Registry.

Answer

ConditionStep

Answer

TransformStep

Answer

TrainingStep

Question 9

During model training on Amazon SageMaker, the training job fails with a 'ResourceLimitExceeded' error. What is the most likely cause?

Accepted Answer

The account's instance limit for the chosen instance type has been reached. The 'ResourceLimitExceeded' error in Amazon SageMaker indicates that the AWS account has reached its service quota for the specified instance type. Each AWS account has default limits on the number of concurrent instances (e.g., ml.p3.2xlarge) that can be used for training jobs. When a training job requests more instances than the account's limit allows, SageMaker throws this error. This is distinct from dataset size or algorithmic issues.

Answer

The algorithm's learning rate is too high

Answer

The dataset is too large for the instance

Answer

The training script has a syntax error

Question 10

A team wants to track and compare multiple machine learning experiments, including hyperparameters, metrics, and artifacts. They are using Amazon SageMaker. Which AWS service or feature should they use to achieve this?

Accepted Answer

Amazon SageMaker Experiments. Amazon SageMaker Experiments is the correct service because it is specifically designed to track and compare machine learning experiments, including hyperparameters, metrics, and artifacts. It provides a structured way to log, organize, and analyze multiple runs, enabling teams to identify the best-performing model configurations.

Answer

AWS CloudTrail

Answer

Amazon SageMaker Model Registry

Answer

Amazon SageMaker Studio

Question 11

A company is using Amazon SageMaker to train a large deep learning model. The training job is taking a very long time. The data scientist suspects that the GPU utilization is low due to inefficient data loading. Which action should the data scientist take to diagnose and address this issue?

Accepted Answer

Check GPU utilization using Amazon CloudWatch metrics, and if low, optimize the data loading pipeline by using Pipe mode or faster data formats.. Low GPU utilization during deep learning training often indicates a data loading bottleneck, where the GPU spends cycles waiting for data. Amazon CloudWatch provides GPU utilization metrics for SageMaker training jobs, and if utilization is low, optimizing the data pipeline with Pipe mode (streaming data directly from Amazon S3) or using faster data formats like RecordIO or TFRecord can reduce I/O overhead and keep the GPU busy.

Answer

Switch to a CPU-only instance to reduce overhead.

Answer

Reduce the batch size to speed up training.

Answer

Increase the number of GPUs in the training instance.

Question 12

A team deploys a PyTorch model on Amazon SageMaker for real-time inference. They notice that inference latency is higher than expected. They suspect the serialization format used for input data is inefficient. Which approach would MOST likely reduce latency?

Accepted Answer

Change the input serialization format to Protocol Buffers.. Protocol Buffers (protobuf) are a binary serialization format that is significantly more compact and faster to parse than text-based formats like JSON or CSV. By reducing the size of the input data and the CPU overhead of deserialization, switching to protobuf directly addresses the root cause of high inference latency on SageMaker real-time endpoints.

Answer

Use Amazon SageMaker Batch Transform instead of real-time inference.

Answer

Enable automatic scaling on the endpoint.

Answer

Increase the instance type to a compute-optimized instance.

Question 13

Refer to the exhibit. A team observes that their SageMaker endpoint scales out quickly when load increases, but scales in very slowly when load decreases, causing over-provisioning. What is the most likely cause?

Accepted Answer

ScaleInCooldown is too high. A high ScaleInCooldown value causes the SageMaker endpoint to wait too long before initiating a scale-in event after load decreases. This delay prevents the endpoint from releasing resources promptly, leading to over-provisioning. In contrast, the scaling out behavior is unaffected by this cooldown, which explains why the endpoint scales out quickly but scales in slowly.

Answer

TargetValue is too high

Answer

ScaleOutCooldown is too low

Answer

Wrong predefined metric selected

Question 14

A company wants to secure access to a SageMaker real-time endpoint. Which TWO actions should be taken? (Select two.)

Accepted Answer

Attach a resource-based policy to the endpoint.. Option B is correct because SageMaker real-time endpoints support resource-based policies (endpoint policies) that let you grant or restrict InvokeEndpoint access to specific principals, AWS accounts, organizations, or source VPC/VPC endpoint conditions, which directly secures who can invoke the endpoint. Option E is correct because configuring the endpoint as private within a VPC and accessing it through an interface VPC endpoint (AWS PrivateLink) keeps invocation traffic off the public internet and enforces network-level isolation. Option A is not correct because sts:AssumeRole is an IAM permission for obtaining temporary credentials, not a mechanism for securing endpoint invocation itself. Option C is not correct because AWS WAF protects HTTP(S) resources like CloudFront, ALB, and API Gateway, and cannot be attached to a SageMaker endpoint. Option D is not correct because CloudTrail provides auditing and logging of API activity, not access control or security enforcement.

Answer

Use an IAM role with sts:AssumeRole for invocation.

Answer

Enable AWS WAF on the endpoint.

Answer

Use AWS CloudTrail to log all invocations.

Question 15

A company is using Amazon SageMaker to train a model on sensitive customer data. The security team requires that all data be encrypted in transit and at rest, and that the training job does not have internet access. Which configuration should the team use to meet these requirements?

Accepted Answer

Configure the training job to run in a private subnet with no internet access, and use a KMS key for encryption. Running the SageMaker training job in a private subnet with no internet access ensures the job cannot reach the public internet, satisfying the no-internet-access requirement. Using an AWS KMS key for encryption at rest (for the S3 bucket and EBS volumes) and enforcing encryption in transit (via HTTPS/TLS for SageMaker and S3 endpoints) meets the encryption requirements. SageMaker training jobs in a private subnet use VPC endpoints (e.g., S3 and SageMaker API endpoints) to communicate securely without internet access.

Answer

Configure the training job to run in a public subnet with a security group that blocks outbound traffic

Answer

Configure the training job to run in a private subnet, but disable encryption to reduce latency

Answer

Configure the training job to run in a VPC with a NAT gateway, and use default SageMaker encryption