MLS-C01 Practice Exam 66 — 20 Questions

Question 1

A data scientist is using Amazon SageMaker to train a model. The training job uses a custom Docker image stored in Amazon ECR. The training job fails with an error 'CannotPullContainerError'. Which TWO actions should the data scientist take to resolve this issue? (Choose TWO.)

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

Accepted Answer

Confirm that the image tag exists in the ECR repository. Options B and C are correct because the error 'CannotPullContainerError' typically indicates that the container image cannot be pulled from ECR. This can occur if the image tag does not exist in the repository (B) or if the IAM role used by the SageMaker training job does not have the necessary permissions (e.g., ecr:GetDownloadUrlForLayer, ecr:BatchGetImage) to pull the image (C). Option A is not required; custom images can be used. Option D, increasing the timeout, would not resolve a pull issue. Option E, internet access, is not necessary because SageMaker can access ECR within the AWS network.

Answer

Use a public Docker image instead of a custom one

Answer

Increase the training job timeout

Answer

Ensure the training instance has internet access

Question 2

A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?

Accepted Answer

Create a stratified train-test split to preserve the class distribution.. Stratified sampling is crucial for highly imbalanced datasets to ensure that the rare class is proportionally represented in both training and testing splits, allowing for proper evaluation. Normalization (A) is important but does not address imbalance. Removing outliers (B) could remove fraud cases. Correlation analysis (D) is useful but not the most critical step for imbalance.

Answer

Normalize all numerical features to have zero mean and unit variance.

Answer

Remove outliers from the dataset using the IQR method.

Answer

Perform correlation analysis to remove highly correlated features.

Question 3

A company is using Amazon SageMaker to deploy a machine learning model that predicts equipment failure. The model is a binary classifier that outputs a probability. The company wants to set a threshold such that the model correctly identifies 95% of actual failures (recall >= 0.95). The model's precision at the current threshold of 0.5 is 0.7. The data scientist evaluates the model on a test set and obtains the following confusion matrix at threshold 0.5: TP=95, FN=5, FP=40, TN=860. The total actual positives are 100. Which threshold adjustment should the data scientist make to achieve the recall goal?

Accepted Answer

Keep the threshold at 0.5. At a threshold of 0.5, recall is TP/(TP+FN) = 95/(95+5) = 0.95, which already meets the goal of recall ≥ 0.95. No adjustment to the threshold is necessary. Decreasing the threshold (e.g., to 0.3 or 0.1) would increase recall but also increase false positives, which is not required. Increasing the threshold (e.g., to 0.7) would reduce recall, missing the goal. Therefore, the correct choice is to keep the threshold at 0.5 (Option C).

Answer

Decrease the threshold to 0.1

Answer

Increase the threshold to 0.7

Answer

Decrease the threshold to 0.3

Question 4

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 5

Drag and drop the steps to perform hyperparameter tuning using SageMaker Automatic Model Tuning in the correct order.

Accepted Answer

Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model.. Tuning involves defining search space, creating a tuning job, setting limits, executing, and selecting best model.

Answer

Define the hyperparameter search space, then configure resource limits, then define the objective metric, then create the tuning job, then monitor and select the best model.

Answer

Select the best model, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job.

Answer

Create the tuning job, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then monitor and select the best model.

Question 6

A data scientist is performing exploratory data analysis on a dataset with 100 features. They want to identify which features are most correlated with the target variable. Which THREE methods are appropriate for this task?

Accepted Answer

Pearson correlation coefficient. Pearson correlation coefficient measures linear relationship between features and target. Feature importance from a random forest provides a ranking of feature relevance. Mutual information captures both linear and non-linear dependencies. Together, these three methods effectively identify correlated features. Variance threshold is used for removing low-variance features, not for correlation. One-hot encoding is a preprocessing technique for categorical variables, not a correlation method.

Answer

Variance threshold

Answer

One-hot encoding

Question 7

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

Accepted Answer

Box plot of the feature grouped by target class. A box plot displays the distribution of the continuous feature for each category of the binary target, allowing easy comparison of medians, spreads, and outliers. This reveals correlation: if the distributions differ notably between classes, the feature is likely correlated with the target. Option A is wrong because a scatter plot is typically used for two continuous variables, not a binary target. Option B is wrong because a histogram shows the distribution of a single continuous variable, ignoring the target. Option D is wrong because a bar chart of target class counts only shows class frequency, not the relationship with the feature.

Answer

Scatter plot with feature on x-axis and target on y-axis

Answer

Histogram of the feature

Answer

Bar chart of target class counts

Question 8

A data scientist is using SageMaker to train a random forest model. The dataset has 100 features and 1 million rows. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause?

Accepted Answer

The account has reached its limit on the number of SageMaker training instances.. The 'ResourceLimitExceeded' error indicates that the account has reached its limit on the number of SageMaker training instances or vCPUs. Option A (S3 bucket region) would cause a different error, not a resource limit. Option B (GPU memory) is unlikely because random forest models typically use CPU instances. Option C (wrong algorithm) would result in an algorithm-specific error, not a resource limit. Option D correctly identifies that the account limit has been exceeded.

Answer

The S3 bucket containing the training data is not in the same region.

Answer

The instance type selected does not have enough GPU memory.

Answer

The wrong algorithm was specified for the training job.

Question 9

A company wants to use Amazon SageMaker to host a model that was trained using a custom algorithm. The model artifact is stored in Amazon S3. The company wants to ensure that the endpoint can automatically scale based on the number of incoming requests. Which configuration should the company use?

Accepted Answer

Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.. A SageMaker real-time endpoint with automatic scaling using a target tracking policy allows the endpoint to dynamically adjust the number of instances based on the incoming request load. This configuration is ideal for hosting a custom algorithm model artifact stored in S3, as it provides low-latency inference and can scale out or in based on a target metric like average CPU utilization or request count per instance.

Answer

Create a SageMaker multi-model endpoint with automatic scaling.

Answer

Use SageMaker Serverless Inference which scales automatically.

Answer

Use SageMaker Batch Transform with a scheduled job.

Question 10

A data scientist is building a binary classifier using logistic regression. The dataset has 10 features and 100,000 observations. The model achieves 99% accuracy on the test set, but the precision is 50% and recall is 90%. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

Accepted Answer

Adjust the decision threshold to increase precision at the cost of recall.. The model has high recall (90%) but low precision (50%), indicating many false positives. Two effective approaches are: adjusting the decision threshold (Option B) to require a higher predicted probability for the positive class, which reduces false positives and increases precision at the cost of some recall. Switching to a random forest classifier (Option C) can capture complex interactions and non-linearities, often improving precision by better separating classes. Option A: Increasing regularization strength (i.e., decreasing C in logistic regression) may help reduce overfitting but does not directly target precision; it may marginally help but is not a primary action. Option D: Collecting more data does not address the underlying class separation issue; it might even amplify imbalance. Option E: Removing features with low correlation could discard valuable information and worsen performance.

Answer

Increase the regularization strength (C) in logistic regression.

Answer

Collect more training data.

Answer

Remove features that have low correlation with the target.

Question 11

A data engineer is analyzing a large dataset stored in Amazon S3 using AWS Glue and Amazon Athena. They notice that queries against a table with many small files are slow. Which TWO actions can improve query performance?

Accepted Answer

Convert files to Apache Parquet format. Converting files to Apache Parquet format (C) improves query performance by leveraging columnar storage, which reduces the amount of data scanned and provides better compression. Using a larger number of partitions (E) allows Athena to perform partition pruning, limiting the data scanned per query. These two actions together reduce the volume of data processed and improve query speed, unlike increasing Glue DPUs (irrelevant for Athena), using automatic compression (not a distinct action), or decreasing partitions (increases scanned data).

Answer

Use Athena's automatic compression

Answer

Increase the number of Glue DPUs

Answer

Decrease the number of partitions

Question 12

A company is building a fraud detection model that must achieve low false positive rates. The dataset is highly imbalanced (0.1% positive class). Which metric is most appropriate for model evaluation?

Accepted Answer

Area under the Precision-Recall curve. In highly imbalanced datasets (0.1% positive class), the Precision-Recall curve focuses on the performance of the positive class, which is the minority class of interest. Area under the Precision-Recall curve (AUPRC) is insensitive to the large number of true negatives, making it a robust metric for evaluating models where false positives must be minimized. Unlike ROC-AUC, which can be overly optimistic in severe imbalance, AUPRC directly reflects the trade-off between precision and recall for the rare positive class.

Answer

RMSE

Answer

Accuracy

Answer

R-squared

Question 13

A company is deploying a model for real-time inference with SageMaker. The endpoint receives spiky traffic, with occasional bursts of 10x normal load. Which scaling policy is MOST cost-effective while maintaining availability?

Accepted Answer

Use a combination of scheduled scaling for predictable peaks and simple scaling for additional bursts.. It combines scheduled scaling for predictable traffic patterns (e.g., known peak hours) with simple scaling to handle unexpected bursts, ensuring availability during 10x load spikes without over-provisioning. This hybrid approach is more cost-effective than always-on large instances, as it dynamically adjusts capacity only when needed, aligning with SageMaker's automatic scaling capabilities.

Answer

Provision a large instance type that can handle the peak load at all times.

Answer

Manually scale the endpoint based on historical traffic patterns.

Answer

Use a target tracking scaling policy based on average latency.

Question 14

A data scientist is using SageMaker to train a model and wants to track experiments, including hyperparameters and metrics. Which TWO actions should the scientist take to set up experiment tracking? (Choose TWO.)

Accepted Answer

Use the SageMaker Experiments Python SDK to create an experiment and log runs.. The SageMaker Experiments Python SDK provides the primary interface for creating and managing experiments, allowing the data scientist to log runs, hyperparameters, and metrics in a structured way. This SDK directly integrates with SageMaker training jobs and notebook executions to capture experiment metadata.

Answer

Enable SageMaker Model Monitor to track training metrics.

Answer

Configure CloudWatch Logs to store experiment data.

Answer

Enable SageMaker Studio to automatically capture experiments.

Question 15

A financial services company uses Amazon SageMaker to train a model for credit risk prediction. The dataset contains 500 features and 1 million records. The target variable is binary with 20% default rate. The data scientist uses a gradient boosting algorithm (XGBoost) with default hyperparameters. After training, the model achieves 95% accuracy, but the precision for the default class is only 30%, and recall is 15%. The business requires at least 50% recall and 40% precision for the default class. The data scientist tries to adjust the decision threshold, but this does not simultaneously meet both targets. The scientist suspects that the model is not learning the default patterns well. The company also has a large dataset of unlabeled transactions that could be used. Which action should the data scientist take to improve the model?

Accepted Answer

Use the unlabeled data for semi-supervised learning with pseudo-labeling.. Using unlabeled data for pseudo-labeling can help the model learn patterns of the minority class by generating additional training examples, which is especially beneficial when the labeled dataset is imbalanced. Option A is incorrect because PCA reduces dimensionality but does not address class imbalance and may discard features important for the default class. Option C is incorrect because increasing the learning rate can cause the model to overshoot optimal minima and may lead to overfitting, not improving recall and precision for the minority class. Option D is incorrect because feature selection reduces the number of features but does not directly address class imbalance; it could even remove features that are relevant for predicting defaults.

Answer

Apply PCA to reduce dimensionality and noise.

Answer

Increase the learning rate to accelerate convergence.

Answer

Reduce the number of features using feature selection to simplify the model.

Question 16

Which TWO options are valid ways to reduce the amount of data scanned by Amazon Athena queries, thereby reducing cost?

Accepted Answer

Use columnar storage formats like Parquet or ORC. A is correct because columnar storage formats like Parquet and ORC store data in a compressed, column-oriented layout. When Athena queries only a subset of columns, it can skip reading the entire row, drastically reducing the amount of data scanned from disk. This directly lowers the cost, as Athena charges based on the volume of data read per query.

Answer

Use LIMIT clause in SQL queries

Answer

Convert data to CSV format

Answer

Create materialized views in Athena

Question 17

A data scientist is deploying a PyTorch model to Amazon SageMaker for real-time inference. The model runs on a large instance but inference latency is too high. Which action is MOST likely to reduce latency without sacrificing accuracy?

Accepted Answer

Compile the model using SageMaker Neo. SageMaker Neo compiles the trained model into an optimized runtime using Apache TVM, applying graph-level optimizations, operator fusion, and memory layout transformations specifically tuned for the target hardware. This reduces inference latency by improving computational efficiency without altering the model's weights or architecture, thus preserving accuracy.

Answer

Switch from a GPU instance to a CPU instance

Answer

Quantize the model weights from FP32 to INT8

Answer

Deploy the model to a multi-model endpoint

Question 18

A company uses SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 8 GB of memory and has high latency. The team wants to reduce latency without increasing cost. Which strategy is most effective?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance.. SageMaker Neo compiles and optimizes the model for the target hardware, reducing latency and memory footprint without increasing cost. Option A is wrong because a larger instance would increase cost. Option B is wrong because deploying on multiple instances increases cost and complexity without necessarily reducing latency. Option D is wrong because batch transform is for offline inference, not real-time, and does not address the low latency requirement.

Answer

Use a larger instance type with more memory.

Answer

Deploy the model on multiple instances behind a load balancer.

Answer

Switch from real-time inference to batch transform.

Question 19

A data scientist is using Amazon SageMaker to train a model. The training job uses a custom Docker image stored in Amazon ECR. The training job fails with an error 'CannotPullContainerError'. Which TWO actions should the data scientist take to resolve this issue? (Choose TWO.)

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

Accepted Answer

The built-in algorithm is not designed to scale with additional vCPUs.. The most likely reason the training time did not decrease significantly is that the built-in algorithm may not be designed to scale effectively with additional vCPUs. Option B correctly identifies this. Option A is less likely because, while single-threaded algorithms cannot use multiple vCPUs, many SageMaker built-in algorithms are parallelized but still have limited scalability due to overhead. Option C is possible but less common; the question's most likely reason is inherent scalability limitations. Option D is incorrect because if the dataset were too small, training time would already be low, and increasing vCPUs could still help, but the lack of improvement here points to scalability issues, not dataset size.

Answer

The algorithm is single-threaded and cannot use multiple vCPUs.

Answer

The training job is I/O bound, and increasing vCPUs does not help.

Answer

The training dataset is too small to benefit from more vCPUs.

Question 20

An IAM policy attached to a SageMaker notebook role is shown. The data engineer tries to run an Athena query on a table in the 'my_database' Glue database. The query fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The policy does not allow athena:StartQueryExecution on the Athena workgroup.. The IAM policy does not include the `athena:StartQueryExecution` action on the Athena workgroup, which is required to submit a query. Even if the role has permissions for Glue and S3, Athena will deny the request if the workgroup-level permission to start queries is missing, resulting in an access denied error.

Answer

The policy does not allow s3:PutObject on the query results location.

Answer

The policy does not allow glue:GetTable on the specific database.

Answer

The policy does not allow s3:ListBucket on the bucket.