MLS-C01 Practice Test 36 — 15 Questions

Question 1

A data scientist is using SageMaker Autopilot to automatically build a classification model. The dataset is highly imbalanced (1% positive class). Which configuration should the scientist set to handle the class imbalance?

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

Accepted Answer

Use the 'AutoMLJobObjective' with 'F1' metric.. SageMaker Autopilot does not support direct class weighting or sample weights for imbalanced datasets. By setting the objective metric to 'F1', Autopilot will optimize the model for the harmonic mean of precision and recall, which is more robust to class imbalance than accuracy. This encourages the model to pay attention to the minority (positive) class during training and hyperparameter tuning.

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Set the problem_type to 'BinaryClassification' and enable 'balance_class_weights'.

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Set the 'sample_weight' attribute in the input data.

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Manually downsample the majority class before training.

Question 2

A team uses SageMaker to train a deep learning model. They notice the training job is using only a fraction of the GPU memory. Which configuration change would most improve GPU utilization?

Accepted Answer

Increase the batch size in the training script. Increasing the batch size allows each training step to process more data samples simultaneously, which increases the computational load per step and better saturates the GPU's parallel processing units. This directly improves GPU memory utilization because larger batches keep more tensors resident in memory and increase the arithmetic intensity of matrix operations, reducing idle time.

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Decrease the batch size to reduce memory fragmentation

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Use a single GPU instead of multiple GPUs

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Enable SageMaker Managed Spot Training

Question 3

A machine learning engineer is setting up a training job in Amazon SageMaker. Which THREE components are required to define a training job? (Choose three.)

Accepted Answer

Output data configuration (e.g., model artifact path).. Output data configuration (e.g., model artifact path) is required because SageMaker needs to know where to save the trained model artifacts (e.g., the model.tar.gz file) in Amazon S3. Without this path, the training job cannot complete successfully as it has no destination for the output.

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VPC configuration for network isolation.

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Hyperparameters for the algorithm.

Question 4

An ML engineer is deploying a model on a SageMaker endpoint and wants to ensure that only authorized users and services can invoke the endpoint. The company uses AWS IAM for access control and requires that the endpoint be invoked only from within a specific VPC. What combination of actions should the engineer take? (Choose the single best answer.)

Accepted Answer

Create a VPC endpoint for Amazon SageMaker and attach a policy that only allows invocation from the VPC. Use IAM roles to restrict which users can invoke the endpoint.. It combines a VPC endpoint for SageMaker with an IAM policy to restrict invocation to authorized users and traffic originating from the specified VPC. A VPC endpoint (interface endpoint) uses AWS PrivateLink to allow private connectivity between the VPC and SageMaker without traversing the public internet, and attaching a resource-based policy to the endpoint ensures only requests from within the VPC are accepted. IAM roles then enforce user-level authorization, meeting both the VPC-only and IAM access control requirements.

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Use AWS CloudFront to restrict access based on IP addresses.

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Use API Gateway in front of the SageMaker endpoint and attach a resource policy to API Gateway.

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Configure network ACLs on the VPC subnet to allow only the endpoint's security group.

Question 5

A data engineer needs to run a one-time ETL job to transform 500 GB of data from Amazon RDS to Amazon S3. The job should be cost-effective and require minimal infrastructure management. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it is a fully managed, serverless ETL service designed for one-time or scheduled data transformation jobs. It automatically provisions and scales the underlying Spark environment, requires no infrastructure management, and charges only for the resources consumed during job execution, making it highly cost-effective for a 500 GB ETL workload from RDS to S3.

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Amazon EMR

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Amazon Athena

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AWS Data Pipeline

Question 6

A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains 100 features and 1 million rows. The scientist notices that the model is overfitting, with training R² of 0.99 and validation R² of 0.65. The scientist has already tried adding L2 regularization and reducing the number of features. Which additional technique should the scientist try to reduce overfitting?

Accepted Answer

Increase the amount of training data. Increasing the amount of training data provides the model with more examples of the underlying distribution, which helps reduce variance and combat overfitting. With 1 million rows and 100 features, the model may still be memorizing noise; adding more diverse data forces the linear regression to generalize better, improving validation R² without changing the model's capacity.

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Increase the batch size

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Increase the learning rate

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Add more features

Question 7

A data scientist is using SageMaker to train a linear learner algorithm. After training, the evaluation shows that the model has high bias. Which action is most likely to reduce bias?

Accepted Answer

Add feature crosses for categorical variables. High bias indicates that the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding feature crosses for categorical variables creates interaction features that allow the linear learner to model non-linear relationships, increasing model complexity and reducing bias. This is a standard technique in linear models to address underfitting without switching to a non-linear algorithm.

Answer

Increase the L2 regularization strength

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Reduce the amount of training data

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Remove some features that have low variance

Question 8

A company uses Amazon EMR to run Spark jobs on a large dataset stored in Amazon S3. The jobs are failing with 'OutOfMemoryError' in the executors. The data is not skewed. Which configuration change will most likely resolve the issue?

Accepted Answer

Increase the spark.executor.memoryOverhead setting. When Spark executors run out of memory during shuffle operations, the `spark.executor.memoryOverhead` setting is often the culprit. This parameter allocates off-heap memory for JVM overhead, internal metadata, and shuffle buffers. Increasing it provides more room for these operations without reducing the executor heap, directly addressing OutOfMemoryError in non-skewed data scenarios.

Answer

Enable Kryo serialization

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Decrease the number of shuffle partitions

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Increase the number of executor cores

Question 9

A data scientist is training a binary classifier to predict customer churn. The dataset has 10,000 samples, with 500 churners (positive class). The scientist trains a logistic regression model and obtains an F1-score of 0.6. To improve the F1-score, which approach is MOST likely to be effective?

Accepted Answer

Apply SMOTE to oversample the minority class. The dataset is highly imbalanced (500 churners out of 10,000 samples, a 5% positive rate). Logistic regression trained on such imbalance tends to bias toward the majority class, resulting in low recall for the minority class and a poor F1-score. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which balances the class distribution and allows the model to learn a better decision boundary, directly improving recall and F1-score.

Answer

Increase the regularization strength (C)

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Apply PCA to reduce feature dimensionality

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Use the original dataset without any modification

Question 10

A company wants to use Amazon SageMaker to automatically tune hyperparameters for a XGBoost model. Which built-in SageMaker feature should be used?

Accepted Answer

SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning performs hyperparameter optimization. Option A (SageMaker Debugger) monitors training. Option B (SageMaker Model Monitor) detects drift. Option C (SageMaker Experiments) tracks trials. Option D (SageMaker Automatic Model Tuning) correctly performs hyperparameter tuning.

Answer

SageMaker Debugger

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SageMaker Model Monitor

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SageMaker Experiments

Question 11

A data scientist is working on a regression problem to predict house prices. The dataset has 80 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The target variable is log-transformed. The data scientist trains a linear regression model and obtains an R² of 0.45 on the test set. To improve performance, the data scientist considers: A) Applying one-hot encoding to all categorical features and using Ridge regression. B) Using target encoding for high-cardinality features and using a tree-based model like XGBoost. C) Removing all categorical features and using polynomial features for numerical features. D) Using principal component analysis (PCA) on all features before training a linear model. Which approach is MOST likely to improve the model's performance?

Accepted Answer

Target encoding + XGBoost. Target encoding efficiently handles high-cardinality features, and tree-based models like XGBoost can capture non-linear relationships and interactions, likely improving R². One-hot encoding would create too many features, causing sparsity. Removing categories loses information. PCA may discard important information.

Answer

Remove categorical features and use polynomial features

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One-hot encoding + Ridge regression

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PCA on all features before linear regression

Question 12

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be transformed before being stored in Amazon S3. The transformations include enrichment with reference data from Amazon DynamoDB. Which AWS service should be used to perform the transformation with minimal operational overhead?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink (Option C) is the correct choice because it provides a fully managed, stateful stream processing engine that can read directly from Kinesis Data Streams, enrich records with reference data from DynamoDB via Flink's Async I/O or JDBC connectors, and write the transformed data to S3—all without provisioning or managing any infrastructure. This minimizes operational overhead compared to self-managed solutions like EMR or Lambda-based architectures that require custom checkpointing and scaling logic.

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Amazon Kinesis Data Firehose with data transformation

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AWS Lambda functions invoked by Kinesis Data Streams

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Amazon EMR with Apache Spark Streaming

Question 13

Drag and drop the steps to create an Amazon SageMaker notebook instance in the correct order.

Accepted Answer

First, open Amazon SageMaker console. Next, select 'Notebook instances' from the left navigation. Then, click 'Create notebook instance'. After that, configure the notebook instance settings (name, instance type, IAM role, VPC). Finally, click 'Create notebook instance'.. Creating a notebook instance requires navigating the SageMaker console, configuring instance settings, IAM role, and VPC, then launching.

Answer

First, open Amazon SageMaker console. Then, click 'Create notebook instance' immediately. Next, configure the notebook instance settings. Finally, select 'Notebook instances' from the navigation.

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First, configure the notebook instance settings (name, instance type, IAM role, VPC). Then, open Amazon SageMaker console. Next, click 'Create notebook instance'. Finally, create the notebook instance.

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First, click 'Create notebook instance'. Then, open Amazon SageMaker console. Next, select 'Notebook instances'. Finally, configure settings and create.

Question 14

Which TWO actions are valid ways to handle missing data in a dataset before training a machine learning model? (Select TWO.)

Accepted Answer

Delete rows with missing values. Deleting rows with missing values (listwise deletion) is a straightforward and valid approach when the missing data is random and the dataset is large enough that the loss of rows does not significantly reduce statistical power or introduce bias. This method avoids the need to estimate missing values and is commonly used in practice when the proportion of missing data is low.

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Remove all features that have any missing values

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Replace missing values with the maximum value

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Ignore missing values and train the model

Question 15

A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.

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Mean squared error

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Accuracy

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Precision