MLS-C01 Practice Questions 2 — 50 Questions

Question 1

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?

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

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class.. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which helps balance the dataset and improves model performance on the minority class without losing information from the majority class. Option A is wrong: training on raw data without addressing imbalance will cause the model to be biased toward the majority class and perform poorly on the minority class. Option C is wrong: accuracy is not a suitable evaluation metric for imbalanced datasets because a model that always predicts the majority class will achieve 99% accuracy, masking poor performance on the minority class; instead, metrics like precision, recall, F1-score, or AUC should be used. Option D is wrong: under-sampling the majority class to match the minority class size discards a large amount of data, potentially losing valuable patterns and reducing model performance.

Answer

Train the model on the raw data without any modification.

Answer

Use accuracy as the evaluation metric and train on the raw data.

Answer

Under-sample the majority class to match the minority class size.

Question 2

A company is using Amazon SageMaker to build a binary classification model for customer churn. The dataset is highly imbalanced (90% no churn, 10% churn). Which technique is MOST effective for handling class imbalance?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class.. SMOTE (Synthetic Minority Oversampling Technique) is the most effective option because it generates synthetic samples for the minority class by interpolating between existing minority instances, thereby balancing the dataset without discarding valuable majority-class data. This approach directly addresses the class imbalance in a binary classification task on SageMaker, improving model recall for the churn class without the information loss caused by undersampling.

Answer

Use accuracy as the evaluation metric.

Answer

Undersample the majority class.

Answer

Train a random forest model instead of logistic regression.

Question 3

Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)

Accepted Answer

Impute missing values using mean or median for numerical features. Options C and D are correct. Imputing missing values using mean or median for numerical features (C) is a common technique during EDA to preserve data size. Visualizing the missing data pattern with heatmaps or bar charts (D) helps understand the distribution and mechanism of missingness. Option A is incorrect because ignoring missing values can introduce bias and lead to inaccurate models. Option B is incorrect because replacing with -1 may distort the data distribution and is not a standard practice. Option E is incorrect because deleting all rows with missing values can cause significant data loss, especially if missingness is not random.

Answer

Ignore missing values and proceed with modeling

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Replace missing values with -1 to indicate missing

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Delete all rows with any missing values

Question 4

A data scientist is training a convolutional neural network (CNN) for image classification using Amazon SageMaker. The training loss decreases steadily but validation loss starts increasing after a few epochs. Which action should the data scientist take to address this issue?

Accepted Answer

Implement early stopping based on validation loss. The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or starts to increase), preventing the model from memorizing noise in the training data. In SageMaker, this can be implemented using the `EarlyStopping` callback in the framework's estimator or by setting `use_early_stopping` to True in a built-in algorithm.

Answer

Increase the learning rate

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Add more convolutional layers

Answer

Increase the batch size

Question 5

A company is building a classification model and discovers that the target variable is imbalanced: 95% of samples belong to class A and 5% to class B. The data scientist needs to understand the distribution of numeric features for each class. Which approach is most appropriate?

Accepted Answer

Use Amazon SageMaker Data Wrangler to create histograms for each feature, grouped by class label.. The most appropriate approach for understanding the distribution of numeric features for each class is to use histograms grouped by the class label. Amazon SageMaker Data Wrangler (option C) can generate these histograms, providing a clear visual comparison of how each numeric feature is distributed across class A and class B. This is especially useful with imbalanced data (95% vs 5%) because it reveals differences in shape, central tendency, and spread without being influenced by class frequencies. Option A (t-test) tests for statistical significance but does not visualize the distribution. Option B (box plots) can show summary statistics but not the full distribution shape as effectively as histograms. Option D (correlation matrix) measures linear relationships with the target but does not show per-class feature distributions.

Answer

Run a t-test for each feature to determine statistical significance between classes.

Answer

Generate box plots for each feature using Amazon QuickSight.

Answer

Compute the correlation matrix between features and the target.

Question 6

A company is using SageMaker to train a model. The training data includes personally identifiable information (PII). The company must ensure that the data is encrypted at rest and in transit. Which combination of actions meets these requirements?

Accepted Answer

Use S3 server-side encryption and enable SageMaker inter-container traffic encryption. It combines S3 server-side encryption (SSE-S3) to protect data at rest in the training dataset with SageMaker inter-container traffic encryption, which encrypts data in transit between training containers using TLS. This dual approach satisfies the requirement for encryption both at rest and in transit within the SageMaker training environment.

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3)

Answer

Enable SSL for data in transit and use VPC endpoints

Answer

Place all resources in a private VPC subnets with no internet access

Question 7

A data scientist is performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)

Accepted Answer

Use multiple imputation to fill in the missing values.. Multiple imputation is a robust statistical technique that accounts for uncertainty in missing values by creating multiple complete datasets, analyzing each, and pooling results. This is particularly appropriate for a dataset with 10,000 rows and 20 features, as it preserves the sample size and avoids bias that simpler methods might introduce.

Answer

Replace missing values with the mode of the feature.

Answer

Identify and remove outliers from the feature.

Answer

Drop the entire feature from the dataset.

Question 8

An organization is migrating its on-premises Hadoop cluster to AWS. The cluster runs Spark jobs that process 50 TB of data daily. The data is stored in HDFS with 3x replication. Which storage option on AWS provides the best price-performance for this workload?

Accepted Answer

Use Amazon EMR with S3 as the data store via EMRFS. Amazon EMR with S3 as the data store via EMRFS provides the best price-performance for this workload because it eliminates the need for 3x replication (S3 is inherently durable and replicated across multiple AZs), reduces storage costs, and allows compute and storage to scale independently. EMRFS enables Spark jobs to read/write directly to S3 with consistency guarantees, matching the throughput requirements of 50 TB daily processing without the overhead of managing HDFS on EBS volumes.

Answer

Use AWS Glue to run Spark jobs with data stored in S3

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Use Amazon Redshift Spectrum to query the data directly in S3

Answer

Use Amazon EMR with HDFS on EBS volumes

Question 9

Which TWO actions are best practices for tuning hyperparameters using Amazon SageMaker Automatic Model Tuning?

Accepted Answer

Use Bayesian optimization strategy. Amazon SageMaker Automatic Model Tuning supports Bayesian optimization, random search, and grid search strategies. Bayesian optimization (Option C) is efficient for finding optimal hyperparameters by exploring promising regions. Random search (Option E) is effective for high-dimensional spaces and often outperforms grid search. Grid search (Option D) is not recommended for many hyperparameters due to combinatorial explosion. Setting a very large number of training jobs (Option A) is costly and unnecessary. Using the same hyperparameters as the baseline model (Option B) does not perform tuning. Therefore, the best practices are Options C and E.

Answer

Set the number of training jobs to a very large value

Answer

Use the same hyperparameters as the baseline model

Answer

Use grid search strategy

Question 10

A data scientist is training a deep learning model for image classification. The model is overfitting on the training data. Which combination of techniques will most effectively reduce overfitting?

Accepted Answer

Add dropout layers and use data augmentation. Dropout layers randomly deactivate a fraction of neurons during training, which forces the network to learn more robust features and prevents co-adaptation. Data augmentation artificially expands the training dataset by applying transformations (e.g., rotation, flipping, cropping), which reduces the model's ability to memorize spurious patterns and improves generalization. Together, these techniques directly counteract overfitting by increasing regularization and effective training diversity.

Answer

Reduce the batch size

Answer

Train for more epochs without early stopping

Answer

Increase the number of layers and neurons

Question 11

A machine learning team is building a model to predict customer churn. The dataset has 20 features and 50,000 rows. After initial EDA, they notice that the target variable 'churn' is highly imbalanced (5% churn, 95% non-churn). Which EDA step should the team prioritize to address this imbalance before model training?

Accepted Answer

Analyze the distribution of each feature separately for churn and non-churn groups.. During EDA for an imbalanced dataset, it is crucial to compare feature distributions between churn and non-churn groups to identify which features separate the classes. Option A is wrong because removing outliers from the majority class is not a standard EDA step and can introduce bias. Option C is wrong because stratified cross-validation is a model evaluation technique applied during training, not an EDA step. Option D is wrong because PCA is a dimensionality reduction technique that does not address class imbalance.

Answer

Remove outliers in the majority class to balance the dataset.

Answer

Perform stratified cross-validation to ensure balanced folds.

Answer

Apply Principal Component Analysis (PCA) to reduce noise.

Question 12

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset. The target column is 'price' (continuous). Which EDA analysis would best help decide between linear regression and tree-based models?

Accepted Answer

Check linear relationships between features and target. Checking linear relationships (e.g., scatter plots of features vs. target) helps determine whether linear regression is appropriate or if tree-based models (which capture non-linear patterns) would perform better. Option A (VIF) is used to detect multicollinearity, which affects linear regression but does not directly guide model selection between linear and tree models. Option C (Z-score) identifies outliers, which is important but not the primary factor for deciding between these model types. Option D (class imbalance) is relevant for classification problems, not regression.

Answer

Compute variance inflation factor (VIF) for features

Answer

Detect outliers using Z-score

Answer

Identify class imbalance in the target

Question 13

A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features and 100,000 observations. The model's training loss is decreasing, but the validation loss starts increasing after a few epochs. Which step should the data scientist take first to address this issue?

Accepted Answer

Reduce the learning rate. The increasing validation loss while training loss decreases is a classic sign of overfitting. Reducing the learning rate (Option B) is the first step to stabilize training by allowing the optimizer to take smaller, more controlled steps, which can help the model converge to a better local minimum and reduce validation loss. In SageMaker, this is typically adjusted via the `learning_rate` hyperparameter in the estimator.

Answer

Add more features to the model

Answer

Increase the batch size

Answer

Increase the number of epochs

Question 14

A data scientist is analyzing a dataset with missing values. The missing data mechanism is missing at random (MAR). Which imputation method is most appropriate to preserve relationships between variables?

Accepted Answer

Use multiple imputation by chained equations (MICE).. Multiple imputation by chained equations (MICE) is well-suited for missing at random (MAR) data as it models each variable with missing values conditional on other variables, preserving relationships. Option A (removing rows) reduces sample size and can introduce bias if data are not MCAR. Option B (KNN) assumes data are missing completely at random (MCAR) and may not handle MAR well. Option D (mean imputation) reduces variance and distorts relationships.

Answer

Remove all rows with any missing values.

Answer

Use k-nearest neighbors imputation.

Answer

Replace missing values with the mean of the column.

Question 15

A data scientist has this IAM policy attached to their IAM role. They are trying to run a SageMaker training job that reads data from 'my-bucket' and writes output to 'my-bucket'. The job fails. What is the most likely reason?

Accepted Answer

Missing iam:PassRole permission. The most likely reason the SageMaker training job fails is that the IAM role lacks the `iam:PassRole` permission. When SageMaker creates a training job, it must assume the execution role specified in the request; without `iam:PassRole`, the service cannot pass the role to itself, causing the API call to fail. This is a common prerequisite for any SageMaker job that requires an execution role.

Answer

The sagemaker:CreateTrainingJob action is not allowed on specific resources

Answer

Missing s3:ListBucket permission on the bucket

Answer

The training job requires permissions to write to CloudWatch Logs

Question 16

A company's ML model training on Amazon SageMaker is taking longer than expected. The training job uses a single ml.p3.2xlarge instance. Which change is most likely to reduce training time?

Accepted Answer

Use distributed training with multiple GPU instances. The training job is bottlenecked by compute capacity, as a single ml.p3.2xlarge instance provides only one NVIDIA V100 GPU. Distributed training with multiple GPU instances (e.g., multiple ml.p3.2xlarge instances) enables data parallelism, splitting the workload across GPUs and significantly reducing wall-clock training time for large models or datasets.

Answer

Increase the instance's EBS volume size

Answer

Enable Managed Spot Training

Answer

Switch to a compute-optimized instance with more vCPUs

Question 17

During EDA, a data scientist finds that a categorical feature 'city' has 500 unique values but only 10 cities account for 90% of the data. What is a recommended way to handle the rare categories?

Accepted Answer

Group rare categories into a single 'Other' category.. Grouping rare categories into 'Other' reduces cardinality, avoids overfitting from high-dimensional sparse features, and retains the majority of data from the top 10 cities. Option B (label encoding) is not recommended as it imposes an arbitrary ordinal relationship that may mislead the model. Option C (one-hot encoding all 500 categories) would create 499 dummy features, leading to the curse of dimensionality and sparse data. Option D (dropping rows with rare categories) discards potentially valuable data and may introduce bias.

Answer

Apply label encoding to all categories.

Answer

One-hot encode all 500 categories.

Answer

Drop all rows with rare categories.

Question 18

A data scientist is training a neural network for image classification. The dataset has 50,000 images across 100 classes. The model uses a ResNet-50 architecture pre-trained on ImageNet. The training loss decreases rapidly, but validation loss starts to increase after 5 epochs. Which of the following is the most effective technique to address this?

Accepted Answer

Use data augmentation to increase the diversity of the training set. The rapid decrease in training loss followed by an increase in validation loss after only 5 epochs is a classic sign of overfitting. Data augmentation artificially expands the training set by applying random transformations (e.g., rotations, flips, crops) to existing images, which improves the model's generalization and reduces overfitting. This is the most effective technique among the options because it directly addresses the lack of diverse training examples without changing the model architecture or training hyperparameters in a way that could destabilize learning.

Answer

Increase the learning rate

Answer

Add more layers to the network

Answer

Use a smaller batch size

Question 19

A team stores raw data in S3 and uses a Glue Data Catalog for metadata. They want to allow data scientists to query the data with Amazon Athena using their existing IAM roles. What is the MINIMUM set of permissions required?

Accepted Answer

Grant the IAM role permissions for Athena actions, Glue Data Catalog actions, and S3 read access.. Athena requires read access to S3 for querying data, permissions to the Glue Data Catalog for schema/metadata resolution, and Athena-specific actions to run queries. Write access to S3 is not required for querying, making B the minimum set.

Answer

Grant the IAM role permissions for Athena, Glue, and S3 (read and write).

Answer

Grant the IAM role permissions for Athena and Amazon Redshift Spectrum.

Answer

Grant the IAM role permissions for Athena and Amazon Kinesis.

Question 20

A data scientist is performing EDA on a dataset with mixed data types (numerical, categorical, text). The dataset is stored in S3. Which TWO AWS services can be used to directly perform statistical summaries and visualizations without writing custom code?

Accepted Answer

Amazon SageMaker Data Wrangler. Options D and E are correct. Amazon SageMaker Data Wrangler provides a visual interface for data preparation and analysis with built-in transforms and visualizations directly on S3 data. Amazon QuickSight is a BI service that connects to S3 and creates dashboards with statistical summaries and visualizations. Option A (SageMaker Studio) is an IDE for ML development, not a direct analysis service without custom code. Option B (AWS Glue DataBrew) is a data preparation tool but requires some configuration and is not primarily for statistical summaries and visualizations. Option C (Athena) is a SQL query engine for querying data, but does not provide built-in visualizations.

Answer

Amazon SageMaker Studio

Answer

AWS Glue DataBrew

Answer

Amazon Athena