MLS-C01 Practice Test 17 — 15 Questions

Question 1

Refer to the exhibit. The training job 'my-job' failed with the error 'Unable to pull image from ECR'. What is the most likely cause?

Accepted Answer

The IAM role does not have permission to pull images from the ECR repository.. The error 'Unable to pull image from ECR' indicates that the SageMaker training job could not retrieve the custom Docker image stored in Amazon ECR. The most likely cause is that the IAM role associated with the training job lacks the `ecr:GetDownloadUrlForLayer` and `ecr:BatchGetImage` permissions required to pull images from the ECR repository. Without these permissions, SageMaker cannot authenticate and download the container image, even if the repository and image exist.

Answer

The instance type ml.m5.large does not support custom images.

Answer

The S3 bucket for training data is in a different account.

Answer

The role ARN is incorrect.

Question 2

A team has trained a deep learning model on Amazon SageMaker using a custom Docker container. They want to deploy the model to a SageMaker endpoint for real-time inference. Which format should the model artifacts be in?

Accepted Answer

A single .tar.gz file containing the model files.. Amazon SageMaker requires model artifacts to be packaged as a single .tar.gz file when using a custom Docker container for real-time inference. This compressed archive must contain the model files (e.g., model.pth, model.h5) and any necessary inference code, as SageMaker extracts the archive to the /opt/ml/model directory during deployment. The .tar.gz format ensures consistent extraction and compatibility with SageMaker's inference pipeline.

Answer

A folder on S3 with the model files.

Answer

No format requirement; any file works.

Answer

A .zip file containing the model files.

Question 3

Which TWO techniques are used for feature scaling? (Choose 2.)

Accepted Answer

Standardization (Z-score normalization). Standardization (Z-score normalization) is a feature scaling technique that transforms data to have a mean of 0 and a standard deviation of 1, using the formula z = (x - μ) / σ. This is essential for algorithms like SVM, k-means, and PCA that assume normally distributed features and are sensitive to feature magnitudes.

Answer

One-hot encoding

Answer

Principal Component Analysis (PCA)

Answer

Label encoding

Question 4

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.. Options A and D are correct. Option A: Data augmentation (e.g., random crops, flips) effectively increases dataset diversity. Option D: Using a pre-trained model (transfer learning) improves generalization. Option B (increasing batch size) may hurt generalization. Option C (increasing learning rate) can cause divergence. Option E (increasing epochs) may lead to overfitting.

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 5

A company is using Amazon SageMaker to train a time series forecasting model using the DeepAR algorithm. The training data contains multiple time series. The model is overfitting. Which action is LEAST likely to reduce overfitting?

Accepted Answer

Reduce the number of time series in the training set.. Option D is correct because reducing the number of time series in the training set reduces the diversity of training data, which typically increases overfitting rather than reducing it. DeepAR relies on learning patterns across multiple related time series to generalize well; fewer time series mean less shared statistical strength, making the model more likely to memorize noise in the remaining series.

Answer

Decrease the number of layers in the neural network.

Answer

Increase the dropout rate.

Answer

Decrease the context length.

Question 6

A company is building a binary classification model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The data scientist uses SMOTE to oversample the minority class. After training a logistic regression model, the recall for the churn class is 0.80, but the precision is only 0.10. Which action would MOST likely improve precision without significantly harming recall?

Accepted Answer

Increase the classification threshold for the positive class. Option A is correct because increasing the classification threshold reduces false positives, improving precision, while still retaining many true positives. Option B is wrong because decreasing the threshold would further reduce precision. Option C is wrong because using a different oversampling technique might not directly address the threshold issue. Option D is wrong because reducing the model's complexity could reduce recall.

Answer

Use random oversampling instead of SMOTE

Answer

Reduce the number of features in the model

Answer

Decrease the classification threshold for the positive class

Question 7

A company is building a machine learning model to detect anomalies in industrial sensor data. The data is time-series with seasonal patterns. The data scientist wants to use Amazon SageMaker to train a model. Which algorithm is most suitable for this task?

Accepted Answer

Random Cut Forest (RCF). Random Cut Forest (RCF) is the most suitable algorithm because it is designed for unsupervised anomaly detection on streaming and time-series data. It works by constructing an ensemble of random trees that isolate anomalies based on how quickly a data point can be separated from the rest, making it effective for detecting outliers in sensor data with seasonal patterns without requiring labeled training data.

Answer

K-Means

Answer

DeepAR

Answer

XGBoost

Question 8

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. Which action would MOST likely reduce training time without sacrificing model accuracy?

Accepted Answer

Enable data parallelism across multiple GPUs. Option C is correct because enabling data parallelism across multiple GPUs distributes the training workload across several devices, allowing larger batch sizes and faster gradient computation per epoch. Amazon SageMaker's distributed training libraries (e.g., SageMaker Data Parallelism) use all-reduce algorithms to synchronize gradients efficiently, which reduces wall-clock training time without altering the model architecture or loss function, thus preserving accuracy.

Answer

Use a smaller instance type for training

Answer

Implement early stopping with a low patience value

Answer

Reduce the number of epochs by half

Question 9

A data scientist is using Amazon SageMaker to train a model. The training job is using a large dataset stored in S3. Which data input mode provides the FASTEST data loading for training?

Accepted Answer

Pipe mode. Pipe mode is the fastest data loading mode for SageMaker training because it streams data directly from S3 to the training algorithm via a FIFO pipe, bypassing disk writes. This eliminates the I/O overhead of downloading files to the local storage, enabling near-zero latency data ingestion for large datasets.

Answer

FastFile mode

Answer

Augmented manifest file

Answer

File mode

Question 10

A data scientist runs a SageMaker training job and receives the above error. The S3 bucket 'my-bucket' contains a folder 'data' with a file 'data.csv'. What is the MOST likely cause of the error?

Accepted Answer

The S3 URI should be s3://my-bucket/data/data.csv instead of s3://my-bucket/data. The error occurs because the SageMaker training job expects a specific S3 object URI (pointing to a file), not a prefix (pointing to a folder). When you specify `s3://my-bucket/data`, SageMaker interprets it as a prefix and attempts to list objects under that prefix, but the training channel requires a direct file reference. Using `s3://my-bucket/data/data.csv` provides the exact object path, allowing SageMaker to download the file correctly.

Answer

The instance type ml.m5.large does not have enough memory

Answer

The VolumeSizeInGB is too small to download the data

Answer

The S3 bucket and the training job are in different regions

Question 11

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 12

A company is training a deep learning model on a large dataset using Amazon SageMaker. The training script uses TensorFlow and requires GPUs. The training job is failing with an out-of-memory error. Which configuration change should be made to resolve this issue?

Accepted Answer

Use a larger instance type with more GPU memory.. The training job is failing with an out-of-memory error, which indicates that the model or batch size exceeds the GPU memory capacity of the current instance. Using a larger instance type with more GPU memory directly addresses this by providing additional VRAM, allowing the model to fit in memory and the training to proceed without failure.

Answer

Increase the number of instances in the training job.

Answer

Switch to using spot instances to reduce cost.

Answer

Enable distributed training across multiple instances.

Question 13

A data scientist is training a binary classification model on a highly imbalanced dataset (0.1% positive class). To improve recall, the team decides to use SageMaker's built-in XGBoost algorithm. Which parameter adjustment is most likely to increase recall without significantly sacrificing precision?

Accepted Answer

Set scale_pos_weight to the ratio of negative to positive samples. Setting scale_pos_weight to the ratio of negative to positive samples (approximately 999:1) tells XGBoost to assign a higher penalty to misclassifications of the minority positive class. This directly increases the gradient contribution from positive samples during training, which shifts the decision boundary to improve recall while maintaining a balance that avoids excessive false positives, thus preserving precision.

Answer

Increase max_depth from 5 to 10

Answer

Reduce num_round from 100 to 50

Answer

Increase subsample from 0.8 to 1.0

Question 14

Which THREE factors should be considered when choosing between SageMaker built-in algorithms and custom algorithms? (Choose THREE.)

Accepted Answer

Custom algorithms allow you to implement any architecture, including proprietary ones. Option A is correct because custom algorithms in SageMaker allow you to implement any architecture, including proprietary or novel models that are not available as built-in algorithms. This flexibility is essential when you need to use a custom neural network, a unique loss function, or a model from a research paper that SageMaker does not natively support.

Answer

Built-in algorithms can only be used with CSV and JSON formats

Answer

Custom algorithms require you to bring your own Docker container, but SageMaker built-in algorithms do not support frameworks like PyTorch

Question 15

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

Accepted Answer

Add dropout layers to the model.. Overfitting occurs when validation loss increases. Dropout is a regularization technique that reduces overfitting. Learning rate decay may help convergence but not specifically for overfitting. Batch normalization helps training stability. Data augmentation is useful but not always applicable.

Answer

Increase the learning rate.

Answer

Remove regularization terms from the loss function.

Answer

Increase the number of epochs.

MLS-C01 Practice Test 17 — 15 Questions

Exhibit

MLS-C01 Practice Test 17 — 15 Questions

Exhibit