MLS-C01 Practice Test 16 — 15 Questions

Question 1

A data engineer created an IAM policy to allow a Glue ETL job to read and write objects to an S3 bucket. The ETL job fails when writing data with the error 'Access Denied'. The job is configured to use SSE-S3 (AES256) encryption. What is the likely issue?

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

Accepted Answer

The policy does not grant s3:PutObject on the bucket itself, which is needed for some write operations.. The error 'Access Denied' when writing to S3 with SSE-S3 encryption typically occurs because the IAM policy lacks the `s3:PutObject` permission on the bucket resource itself. While the policy may grant `s3:PutObject` on the object ARN (`arn:aws:s3:::bucket/*`), some S3 write operations—especially those involving encryption headers or bucket-level checks—also require the permission on the bucket ARN (`arn:aws:s3:::bucket`). Without this, the request is denied even if the object-level permission exists.

Answer

The policy grants s3:PutObject on all buckets, not just the specific one.

Answer

The condition requires objects to be encrypted with SSE-KMS, but the job uses SSE-S3.

Answer

The condition requires objects to use SSE-S3, but the job uses SSE-KMS.

Question 2

A data scientist is training a linear regression model on a dataset with 10 features. After training, the model has high variance on the test set. Which technique should the data scientist use to reduce variance without significantly increasing bias?

Accepted Answer

Use L2 regularization. L2 regularization (Ridge regression) adds a penalty term proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces model complexity and variance by preventing any single feature from having an overly large influence, without eliminating features entirely, thus keeping bias relatively low.

Answer

Add more features

Answer

Use a simpler model

Answer

Use a deeper decision tree

Question 3

A data scientist is developing a deep learning model for object detection using Amazon SageMaker. The training dataset has 50,000 labeled images. The data scientist wants to improve model generalization without collecting more data. Which TWO techniques can be applied? (Choose two.)

Accepted Answer

Apply data augmentation techniques such as random cropping and horizontal flipping.. Data augmentation techniques like random cropping and horizontal flipping artificially expand the training dataset by generating modified versions of existing images. This exposes the model to more varied input patterns, reducing overfitting and improving generalization without requiring new labeled data.

Answer

Increase the learning rate to speed up convergence.

Answer

Increase the number of training epochs to ensure convergence.

Answer

Increase the batch size to reduce variance.

Question 4

A team is using SageMaker to train a custom PyTorch model on a large dataset (10 TB) stored in S3. The training job is repeatedly failing due to 'OutOfMemory' errors on the GPU. The team is using a single ml.p3.8xlarge instance. Which change is most likely to resolve the issue?

Accepted Answer

Reduce the batch size in the training script. The 'OutOfMemory' error on the GPU indicates that the model and its associated data exceed the available GPU memory. Reducing the batch size directly decreases the memory footprint per training step, allowing the model to fit within the GPU's memory limits. This is the most direct and effective fix for GPU OOM errors, as it reduces the amount of data processed simultaneously without changing the instance type or input mode.

Answer

Change the instance type to ml.p3.16xlarge (more GPUs)

Answer

Use managed spot training to reduce cost

Answer

Switch the input mode from Pipe to File

Question 5

A data scientist attempts to create a SageMaker training job using the IAM policy shown in the exhibit. The training job fails with an access denied error. What is the most likely cause?

Accepted Answer

The IAM policy is missing the s3:ListBucket action. The IAM policy shown in the exhibit grants s3:GetObject and s3:PutObject actions but is missing the s3:ListBucket action. When SageMaker attempts to read or write objects in an S3 bucket, it first needs to list the bucket's contents to verify the object's existence and path. Without s3:ListBucket, the training job fails with an access denied error during the initial S3 operation.

Answer

The S3 bucket policy does not grant access to the SageMaker service principal

Answer

The IAM policy is missing the sagemaker:DescribeTrainingJob permission

Answer

The IAM policy is missing the s3:PutObject permission

Question 6

A data scientist is building a text classification model using a bag-of-words approach with logistic regression. The dataset has 10,000 documents and 50,000 unique tokens. The model overfits. Which TWO techniques can help reduce overfitting?

Accepted Answer

Reduce the vocabulary size by removing rare and very frequent terms. Removing rare and very frequent terms reduces the feature space and eliminates noise, which helps the logistic regression model generalize better. Rare terms often act as noise that the model can latch onto for spurious correlations, while very frequent terms (like stopwords) provide little discriminative power. This dimensionality reduction directly combats overfitting by simplifying the model.

Answer

Increase the number of n-grams features

Answer

Use one-hot encoding instead of bag-of-words

Answer

Use a more complex model such as a neural network

Question 7

A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?

Accepted Answer

Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.. Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.

Answer

Use AWS Step Functions to orchestrate retraining, but require a manual approval step.

Answer

Use SageMaker training jobs manually triggered by the team each day.

Answer

Use a cron job on an EC2 instance to run a training script.

Question 8

A company is building a recommendation system using Amazon SageMaker's Factorization Machines algorithm. The dataset includes user IDs, item IDs, and ratings. The data is sparse. Which data format should be used for training?

Accepted Answer

RecordIO-protobuf format with sparse feature vectors.. Factorization Machines (FM) in SageMaker are optimized for sparse, high-dimensional data. The RecordIO-protobuf format allows you to directly specify sparse feature vectors using integer keys and float values, which avoids the memory overhead of dense representations and enables efficient distributed training. This format is the recommended input for SageMaker's built-in FM algorithm.

Answer

CSV format with one row per rating.

Answer

JSON lines format with nested structures.

Answer

Parquet format with columns for each feature.

Question 9

Which TWO of the following are best practices for training deep learning models on Amazon SageMaker? (Select TWO.)

Accepted Answer

Use Pipe input mode to stream data directly from S3 to the algorithm.. SageMaker's Pipe input mode streams training data directly from Amazon S3 to the algorithm without writing it to disk, reducing I/O latency and eliminating the need for large local storage. This is especially beneficial for deep learning models that iterate over large datasets, as it allows training to start faster and avoids the overhead of downloading data to EBS volumes.

Answer

Use SageMaker Processing to perform data augmentation before training.

Answer

Store training data on Amazon EBS volumes attached to the training instance.

Answer

Disable checkpointing to improve training speed.

Question 10

A data scientist is training a linear regression model and notices high bias in the training set. What action is most likely to reduce bias?

Accepted Answer

Add more relevant features to the model.. High bias indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases the model's capacity to learn complex relationships, directly reducing bias. This is a standard approach in linear regression to address underfitting.

Answer

Apply L1 regularization.

Answer

Increase the learning rate.

Answer

Increase the amount of training data.

Question 11

A data scientist is working with a dataset that contains a 'Price' column. After plotting a histogram, they observe that the distribution is right-skewed with many extreme high values. They plan to use a linear model that assumes normally distributed errors. Which of the following transformations should they apply to the 'Price' column to make it more normally distributed?

Accepted Answer

Apply log transformation (log(Price)).. Log transformation is commonly applied to right-skewed data to reduce skewness and make the distribution more normal, which is suitable for linear models assuming normally distributed errors. Option B (square transformation) exacerbates skewness, making it worse. Option C (min-max scaling) only rescales the data to a fixed range and does not change the shape of the distribution. Option D (binning) discards information and does not transform the distribution to be normal.

Answer

Apply square transformation (Price^2).

Answer

Apply min-max scaling to the 'Price' column.

Answer

Bin the 'Price' values into equal-width intervals.

Question 12

A company is using Amazon SageMaker to train a model using a custom Docker container. The training script writes model artifacts to the `/opt/ml/model` directory. The training job completes successfully, but the model artifacts are not uploaded to the S3 output path specified in the training job. The company has verified that the SageMaker execution role has the necessary S3 permissions. The Docker container is built using a base image that is not one of the official SageMaker Docker images. What is the MOST likely reason for the failure to upload model artifacts?

Accepted Answer

The custom container does not include the SageMaker training toolkit, which handles artifact uploads.. The SageMaker training toolkit is required in custom containers to handle the automatic upload of model artifacts to S3. Without it, even if the script writes to /opt/ml/model and has correct permissions, SageMaker cannot perform the upload. Option B is correct because the container lacks the toolkit. Option A is incorrect because the entry point is unrelated to the upload failure. Option C is incorrect as the output path formatting would cause a different error. Option D is incorrect because permissions were already verified.

Answer

The training script's entry point is not correctly specified in the container.

Answer

The output path in the training job configuration is incorrectly formatted.

Answer

The SageMaker execution role does not have s3:PutObject permission on the output bucket.

Question 13

Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. When the data scientist tries to run a training job that writes model artifacts to 's3://my-bucket/models/', the job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

Missing s3:PutObject permission for the output S3 bucket. The error occurs because the IAM policy attached to the SageMaker notebook instance role does not grant the s3:PutObject permission on the 's3://my-bucket/models/' path. SageMaker training jobs require this permission to write model artifacts to the specified S3 output bucket. Without it, the API call to upload the model fails with an access denied error, even if other S3 actions are allowed.

Answer

The IAM role does not have a trust policy

Answer

The policy does not include any S3 actions

Answer

The sagemaker:CreateTrainingJob action is not allowed on the specific resource

Question 14

A data scientist is training a deep learning model on a large dataset using SageMaker. The training job is taking too long. Upon reviewing the CloudWatch logs, the scientist notices that the GPU utilization is below 10% most of the time. Which change is MOST likely to improve GPU utilization and reduce training time?

Accepted Answer

Increase the batch size in the training script.. Low GPU utilization (below 10%) indicates that the GPU is idle most of the time, waiting for data to be fed. Increasing the batch size allows each training step to process more samples per forward/backward pass, keeping the GPU busy with larger matrix operations and reducing the relative overhead of data loading and kernel launches. This directly improves GPU throughput and reduces total training time.

Answer

Use a different optimizer that requires less computation.

Answer

Switch to a smaller instance type to reduce data transfer overhead.

Answer

Reduce the size of the training dataset.

Question 15

A company wants to use Amazon SageMaker to train a model using data stored in Amazon S3. The data is sensitive and must be encrypted at rest and in transit. Which THREE steps should be taken to ensure data security?

Accepted Answer

Configure the SageMaker training job to use an IAM role with least privilege and enable network isolation. Configuring the SageMaker training job with an IAM role that follows least privilege ensures that only necessary permissions are granted, reducing the risk of unauthorized access. Enabling network isolation prevents the training job from accessing the internet, which mitigates data exfiltration risks and ensures that data remains within the controlled AWS environment.

Answer

Store the data in Amazon Redshift instead of S3

Answer

Allow internet access for the SageMaker notebook instance