MLS-C01 Machine Learning Implementation and Operations Practice Test 14 — 15 Questions

Question 1

An ML engineer is troubleshooting why an automated CI/CD pipeline cannot deploy an updated model to an existing SageMaker endpoint. The pipeline uses the IAM role that has the attached policy shown in the exhibit. What is the MOST likely cause of the failure?

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

Accepted Answer

The pipeline tries to update an existing endpoint, but the sagemaker:UpdateEndpoint action is not allowed.. The pipeline is attempting to deploy an updated model to an existing SageMaker endpoint, which requires the sagemaker:UpdateEndpoint action. The IAM policy shown in the exhibit (not provided here but implied) does not include this action, so the API call fails with an access denied error. Without explicit permission to update the endpoint, the CI/CD pipeline cannot modify the deployed configuration.

Answer

The pipeline tries to create a new endpoint, but the sagemaker:CreateEndpoint action is denied.

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The pipeline tries to delete the old endpoint, but the sagemaker:DeleteEndpoint action is denied by a Deny statement.

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The pipeline attempts to invoke the endpoint, but the sagemaker:InvokeEndpoint action is denied.

Question 2

Drag and drop the steps to train a model using Amazon SageMaker built-in algorithm in the correct order.

Accepted Answer

Prepare training data and upload to S3 → Choose built-in algorithm → Create training job specifying algorithm, input/output S3 paths, and compute resources → Execute training job. Training involves data preparation, job creation, algorithm selection, input/output paths, and execution.

Answer

Choose built-in algorithm → Prepare training data and upload to S3 → Create training job specifying algorithm, input/output S3 paths, and compute resources → Execute training job

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Prepare training data and upload to S3 → Create training job specifying input/output S3 paths and compute resources → Choose built-in algorithm → Execute training job

Answer

Create training job specifying algorithm, input/output S3 paths, and compute resources → Prepare training data and upload to S3 → Execute training job → Choose built-in algorithm

Question 3

A data scientist is training a linear regression model on a dataset with 100 features. The model shows high variance on the test set. Which action is MOST likely to reduce overfitting?

Accepted Answer

Apply L2 regularization (Ridge regression). High variance (overfitting) means the model is too complex and fits noise in the training data. L2 regularization (Ridge regression) adds a penalty proportional to the square of the coefficients, shrinking them toward zero and reducing model complexity. This directly counteracts overfitting by preventing the model from relying too heavily on any single feature.

Answer

Use a more complex model like XGBoost

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Increase the number of training iterations

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Add more feature engineering to increase model complexity

Question 4

A machine learning team is building a fraud detection system using Amazon SageMaker. The training data is highly imbalanced (99% legitimate, 1% fraudulent). They need to maximize the recall of the fraud class while keeping precision above 90%. Which approach should they take?

Accepted Answer

Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%. Adjusting the decision threshold on the validation set directly optimizes the trade-off between recall and precision. By lowering the threshold, the model classifies more instances as fraud, increasing recall, while the precision constraint (≥90%) ensures the threshold is set at a point where false positives remain acceptably low. This approach works with any probabilistic classifier and does not alter the training data distribution.

Answer

Undersample the majority class to create a balanced dataset and train a Random Forest

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Train an XGBoost model with scale_pos_weight parameter set to 99

Answer

Use SMOTE to oversample the fraud class and then train a logistic regression

Question 5

A data scientist wants to use Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is expected to take several hours. Which storage option should be used to minimize data loading time and cost?

Accepted Answer

Use Pipe mode to stream data directly from S3 during training. Pipe mode is the correct choice because it streams data directly from S3 into the training algorithm without writing to disk, eliminating the time and cost of copying large datasets to the instance's local storage. This minimizes data loading time (streaming starts immediately) and cost (no EBS volume or additional storage charges), making it ideal for large datasets that take hours to train.

Answer

Attach an Amazon EBS volume with the dataset pre-loaded

Answer

Use File mode to copy data to the training instance's local storage

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Mount an Amazon EFS file system to the training instance

Question 6

An ML engineer is deploying a model to a SageMaker endpoint for real-time inference. The model requires a custom inference script that preprocesses input data and postprocesses predictions. Which SageMaker feature should be used to implement this custom logic?

Accepted Answer

Create a SageMaker model with a custom inference script that includes pre- and post-processing functions. SageMaker allows you to bring your own container or use a pre-built container with a custom inference script that defines `input_fn`, `predict_fn`, `output_fn`, and `model_fn` functions. These functions handle preprocessing of input data, model prediction, and postprocessing of predictions, enabling custom logic for real-time inference endpoints without requiring separate infrastructure.

Answer

Use SageMaker Ground Truth to transform inference requests

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Use SageMaker Processing jobs to preprocess data before inference

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Use a built-in SageMaker algorithm with the default inference code

Question 7

A data science team is deploying a machine learning model to production using Amazon SageMaker. The model requires real-time inference with low latency. Which SageMaker feature should they use to deploy the model?

Accepted Answer

SageMaker Realtime Endpoint. SageMaker Realtime Endpoints are designed for low-latency, synchronous inference, making them the correct choice for serving predictions in real time. They keep the model loaded and ready to respond to individual requests, which is essential for applications requiring immediate responses.

Answer

SageMaker Notebook Instance

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SageMaker Batch Transform

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SageMaker Autopilot

Question 8

During training of a deep learning model on a GPU instance in SageMaker, the training job fails with an insufficient memory error. Which step should be taken first to resolve this issue?

Accepted Answer

Reduce the batch size. The most direct cause of an out-of-memory (OOM) error during GPU training is that the combined size of the model parameters, activations, and gradients exceeds the GPU's VRAM. Reducing the batch size immediately decreases the memory footprint of activations stored for backpropagation, which is the largest and most tunable memory consumer. This is the first and simplest step to resolve the error without altering the model architecture or training dynamics.

Answer

Add dropout layers

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Use a smaller learning rate

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Use gradient clipping

Question 9

A company uses SageMaker to train a model each night. The training data is stored in an S3 bucket with SSE-S3 encryption. The training job fails with an access denied error. Which configuration is needed?

Accepted Answer

Create an IAM role with S3 read access and assign it to the SageMaker training job. SageMaker training jobs assume an IAM role that grants permissions to access input data in S3 and write output artifacts. If the role lacks s3:GetObject on the training bucket, the job fails with Access Denied. Creating an IAM role with S3 read access and assigning it to the training job is the required configuration.

Answer

Configure the training job to run in a VPC with S3 VPC Endpoint

Answer

Enable SSE-KMS on the S3 bucket

Answer

Add a bucket policy allowing s3:GetObject for all principals

Question 10

A data scientist is deploying a model to a SageMaker endpoint and needs to optimize for cost while maintaining low latency. Which TWO actions should the data scientist take?

Accepted Answer

Use SageMaker Serverless Inference. SageMaker Serverless Inference (D) is correct because it automatically provisions and scales compute capacity based on traffic, charging only for the compute time used during request processing, which directly reduces cost for intermittent or unpredictable workloads while still providing low-latency real-time responses. Enabling Auto Scaling on the endpoint (E) is correct because it dynamically adjusts the number of instances to match demand, avoiding over-provisioning during low-traffic periods and maintaining low latency during spikes, thereby optimizing cost without sacrificing responsiveness. A larger instance type (A) increases hourly cost and does not inherently improve cost efficiency, so it does not belong. Deploying to a single instance (B) may reduce cost but eliminates redundancy and cannot scale to maintain low latency under load, so it is not a recommended optimization. Switching to batch transform (C) is for offline, non-real-time inference and cannot serve a live endpoint with low latency, so it is inappropriate here.

Answer

Use a larger instance type

Answer

Deploy to a single instance

Answer

Switch to batch transform

Question 11

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The training job fails because the input data is not accessible. Which step should the data scientist take to fix the issue?

Accepted Answer

Attach an IAM policy to SageMakerRole that grants s3:GetObject on the bucket. The training job fails because the SageMaker execution role (SageMakerRole) lacks the necessary IAM permissions to read the input data from the S3 bucket. By attaching an IAM policy that grants s3:GetObject on the specific bucket, the role gains the required access. SageMaker uses the execution role to access S3 data, so the role's permissions must explicitly allow the GetObject action.

Answer

Add a VpcConfig to the training job

Answer

Modify the bucket policy to allow s3:GetObject for any principal

Answer

Increase VolumeSizeInGB to 50

Question 12

Refer to the exhibit. A SageMaker training job uses an IAM role with this policy. The training job writes output to s3://my-bucket/output/. Which statement about the policy is true?

Accepted Answer

The training job can write output objects only if server-side encryption with SSE-S3 is used. The policy includes a Deny statement that explicitly denies PutObject requests unless the request includes the `x-amz-server-side-encryption` header set to `AES256`, which corresponds to SSE-S3. The Allow statement grants PutObject permission, but the Deny statement overrides it for any request that does not meet the encryption condition. Therefore, the training job can only write output objects if server-side encryption with SSE-S3 is used.

Answer

The Allow statement allows all PutObject requests regardless of encryption

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The Deny statement blocks all PutObject requests

Answer

The GetObject permission requires the object to be encrypted with SSE-S3

Question 13

A data scientist needs to create a SageMaker notebook instance with access to a private S3 bucket. The bucket uses SSE-KMS encryption. Which additional configuration is required?

Accepted Answer

Attach a policy to the notebook's IAM role that allows kms:Decrypt. When an S3 bucket uses SSE-KMS encryption, the SageMaker notebook instance's IAM role must include a policy that allows the kms:Decrypt action. This is necessary because SageMaker needs to decrypt the data using the KMS key when reading objects from the bucket. Without this permission, the notebook instance will fail to access the encrypted S3 objects, even if the bucket policy allows s3:GetObject.

Answer

Add a lifecycle configuration script

Answer

Modify the bucket policy to allow s3:GetObject

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Place the notebook instance in a VPC

Question 14

A company uses SageMaker to run training jobs on a schedule. The training data is stored in an S3 bucket that receives new data every hour. Which TWO approaches can the company use to trigger a training job when new data arrives?

Accepted Answer

Set up an Amazon EventBridge rule that triggers on S3 Object Created events and targets a Lambda function. Option B is correct because Amazon EventBridge natively supports S3 events (Object Created) as an event source, and an EventBridge rule can match those events and invoke a Lambda function as a target, which can then call the SageMaker CreateTrainingJob API to start the training job when new data lands. Option E is correct because S3 event notifications can be configured directly on the bucket to invoke a Lambda function on s3:ObjectCreated:* events, and that Lambda function can start the SageMaker training job, providing a direct event-driven trigger. Option A is not appropriate because SQS buffering plus polling from a training instance is a pull-based pattern that does not itself trigger a job on arrival and adds unnecessary complexity. Option C is wrong because a scheduled Step Functions execution is time-based, not event-driven, so it cannot react to new data arriving. Option D is wrong because CloudWatch Logs monitoring S3 access logs is indirect, delayed, and not a supported real-time trigger mechanism for object creation events.

Answer

Use an SQS queue to buffer S3 events and poll from a training instance

Answer

Use AWS Step Functions with a scheduled execution

Answer

Use CloudWatch Logs to monitor S3 access logs and trigger a Lambda function

Question 15

A data scientist needs to deploy a PyTorch model for real-time inference. Which AWS service is best suited for this task?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are purpose-built for hosting ML models that require low-latency, synchronous inference. They automatically manage the underlying infrastructure, including scaling, load balancing, and health checks, and support custom PyTorch containers via the SageMaker inference toolkit. This makes them the optimal choice for deploying a PyTorch model for real-time inference.

Answer

Amazon SageMaker Batch Transform

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Amazon ECS with Fargate

Answer

AWS Lambda with custom container