MLS-C01 Practice Test 29 — 15 Questions

Question 1

A data scientist is tuning hyperparameters for an XGBoost model on a large dataset using Amazon SageMaker. The training job is taking too long, and they want to speed up the tuning process. Which strategy is most effective?

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

Accepted Answer

Use Bayesian optimization. Bayesian optimization is a sequential model-based optimization technique that uses a probabilistic model to guide the search for optimal hyperparameters. It is more sample-efficient than grid or random search, often finding better hyperparameters in fewer iterations, thus speeding up the tuning process. This is especially effective for large datasets where each training run is expensive.

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Use grid search with a fine-grained grid

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Use random search with more iterations

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Reduce the max depth of trees

Question 2

A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, year built, and location. After training a linear regression model, the data scientist notices that the residuals have a clear pattern when plotted against predicted values: they increase with predicted values. The model also has high RMSE. Which action should the data scientist take to improve the model?

Accepted Answer

Apply a log transformation to the target variable.. The residuals increasing with predicted values indicate heteroscedasticity, which violates a core linear regression assumption and inflates RMSE. When the target (house prices) is right-skewed and its variance grows with magnitude, a log transformation stabilizes the variance and makes the relationship more linear, directly addressing the pattern observed.

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Remove outliers from the dataset.

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Use L1 regularization (Lasso) to reduce overfitting.

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Add interaction terms between features.

Question 3

A data scientist is analyzing a dataset with many categorical features. The target variable is binary. Which statistical test should be used to assess the association between each categorical feature and the target?

Accepted Answer

Chi-squared test of independence. The Chi-squared test of independence is the appropriate test to assess association between two categorical variables. Here, both the features (categorical) and the target (binary, which is categorical) are categorical, making the Chi-squared test the correct choice. Option A (Pearson correlation) is for continuous variables, not categorical. Option C (ANOVA) compares means across groups for a continuous target, but our target is binary (categorical). Option D (Kolmogorov-Smirnov test) compares distributions of continuous variables, not categorical. Therefore, B is correct.

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Pearson correlation coefficient

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ANOVA

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Kolmogorov-Smirnov test

Question 4

A data scientist is performing exploratory data analysis on a dataset with missing values. Which TWO approaches are appropriate for handling missing data in a way that retains as much data as possible?

Accepted Answer

Use a model that handles missing values natively, such as XGBoost. Option A is correct because algorithms like XGBoost implement a sparsity-aware split-finding method that learns a default direction for missing values at each tree node, so no rows or columns need to be discarded and all available data is retained. Option C is correct because median imputation replaces missing entries with the column's median, preserving every row and column while remaining robust to outliers and skew, which suits exploratory data analysis. Option B does not belong because substituting a sentinel like -999 distorts the feature's distribution and can be misinterpreted as a real numeric value by many models. Option D does not belong because dropping columns with a high missing percentage discards potentially useful features and the data they do contain. Option E does not belong because dropping every row with any missing value (listwise deletion) removes the most data and can introduce serious bias.

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Replace missing values with a constant like -999

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Drop columns with high missing percentage

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Drop rows with any missing value

Question 5

A company uses Amazon Redshift for data warehousing. The data engineering team notices that query performance has degraded over time. Which THREE actions should the team take to improve performance? (Choose THREE.)

Accepted Answer

Define appropriate sort keys on large tables. Sort keys in Amazon Redshift define the order in which data is stored on disk within each node. By defining appropriate sort keys on large tables, the query engine can use zone maps to skip entire blocks of data that do not match the query's filter predicates, dramatically reducing the amount of data scanned and improving query performance.

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Increase the number of nodes in the Redshift cluster

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Delete old data that is no longer needed

Question 6

A company is using Amazon Redshift for data warehousing. The data engineering team notices that queries are slow and the system is frequently writing to disk due to insufficient memory. Which type of workload management (WLM) configuration change would help reduce disk writes?

Accepted Answer

Increase the memory percentage allocated to the WLM queue.. When queries spill to disk in Amazon Redshift, it indicates that the memory allocated to the WLM queue is insufficient for the workload. Increasing the memory percentage for the queue allows more queries to be processed in memory, reducing the need to write intermediate results to disk and improving query performance.

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Increase the number of query concurrency slots.

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Enable query monitoring rules to abort queries that spill to disk.

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Enable short query acceleration (SQA).

Question 7

A machine learning engineer needs to deploy a model that requires low latency (under 10 ms) for real-time inference. The model is a small ensemble of decision trees. Which Amazon SageMaker endpoint configuration is MOST appropriate?

Accepted Answer

Real-time endpoint. Real-time endpoints in Amazon SageMaker are designed for low-latency inference (typically under 10 ms) and are the correct choice for deploying a small ensemble of decision trees that needs to respond to individual prediction requests in real time. They keep the model loaded and ready, providing a persistent HTTPS endpoint that can serve predictions with minimal overhead.

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Batch transform

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Training job

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Multi-model endpoint

Question 8

A data scientist is using Amazon SageMaker to train an XGBoost model for a regression problem. The training data contains missing values in some features. Which approach should the data scientist use to handle missing values in XGBoost?

Accepted Answer

Leave missing values as-is; XGBoost handles them natively. XGBoost has a built-in mechanism to handle missing values natively by learning the best direction to split on missing values during training. For each split, XGBoost assigns missing values to the left or right child node based on which direction minimizes the loss function, making explicit imputation unnecessary for this algorithm.

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Use K-nearest neighbors imputation

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Remove all rows with missing values

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Impute missing values with the mean of the column

Question 9

A data scientist is building a time series forecasting model for daily sales data. The data exhibits strong seasonality with a weekly pattern and a yearly trend. The scientist wants to use Amazon SageMaker's built-in algorithm. Which algorithm is most appropriate?

Accepted Answer

Amazon SageMaker DeepAR. Amazon SageMaker DeepAR is a built-in algorithm specifically designed for time series forecasting. It handles complex patterns like seasonality (weekly) and trends (yearly) by using recurrent neural networks (RNNs) to model the probabilistic distribution of future values. DeepAR is well-suited for univariate and multivariate time series and can incorporate additional features, making it ideal for daily sales data with strong seasonal and trend components.

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Linear Learner

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K-Means

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XGBoost

Question 10

Which TWO actions should a data scientist take when exploring a dataset that contains missing values and outliers? (Select TWO.)

Accepted Answer

Calculate the percentage of missing values per column.. Calculating the percentage of missing values per column is a standard first step in exploratory data analysis (EDA) to quantify data completeness. This informs downstream decisions such as whether to impute, drop, or flag missing data, and helps assess the risk of bias or information loss. It is a diagnostic action, not a transformation, and should precede any imputation or removal.

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Normalize all features using Min-Max scaling.

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Remove all rows with outliers.

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Impute missing values with the mean immediately.

Question 11

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.

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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.

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

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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 12

An IAM policy attached to a SageMaker notebook role is shown in the exhibit. A data scientist is trying to run a training job from the notebook, but the job fails with an access denied error. The training job needs to read data from 'my-bucket' and write output to 'my-bucket'. What is the most likely cause of the failure?

Accepted Answer

The training job execution role does not have the same permissions. The IAM policy shown is attached to the SageMaker notebook role, which is used by the data scientist to interact with the notebook. However, when a training job is launched, it runs under a separate execution role (the SageMaker execution role for training jobs), not the notebook role. The training job fails because that execution role lacks the necessary S3 permissions (e.g., s3:GetObject, s3:PutObject) to read from and write to 'my-bucket'. The notebook role's permissions are irrelevant to the training job's runtime actions.

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The policy does not allow s3:ListBucket

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The policy does not allow sagemaker:CreateTrainingJob

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The S3 bucket is not specified in the Resource

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The policy does not allow s3:GetObject

Question 13

A data scientist needs to evaluate a binary classification model's performance. Which metric is most appropriate when the cost of false positives is very high?

Accepted Answer

Precision. Precision is the most appropriate metric when the cost of false positives is very high because it measures the proportion of positive identifications that were actually correct. In binary classification, precision = TP / (TP + FP), so a high precision means very few false positives occur, directly minimizing the costly error type.

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Accuracy

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F1 score

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Recall

Question 14

A team is training a large language model using SageMaker's distributed training. They notice that the training loss is not decreasing after the first few epochs. Which action is MOST likely to resolve this issue?

Accepted Answer

Reduce the learning rate. When training loss plateaus or does not decrease after the first few epochs, a common cause is that the learning rate is too high, causing the optimizer to overshoot minima. Reducing the learning rate helps the model converge. Increasing the batch size (option A) mainly affects gradient variance and training speed but does not address an overly large step size. Adding L2 regularization (option B) helps prevent overfitting but does not resolve a high learning rate. Switching from Adam to SGD (option D) may not help because Adam typically adapts learning rates per parameter; if the base learning rate is too high, both optimizers can struggle. Therefore, reducing the learning rate is the most direct and effective action.

Answer

Increase the batch size

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Add L2 regularization

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Switch from Adam to SGD optimizer

Question 15

A machine learning team is working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?

Accepted Answer

Truncated Singular Value Decomposition (SVD).. Truncated Singular Value Decomposition (SVD) is specifically designed for sparse matrices and can reduce dimensionality without densifying the data. It computes the top k singular values and vectors, preserving the sparsity structure and making it efficient for high-dimensional sparse features like bag-of-words.

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t-distributed Stochastic Neighbor Embedding (t-SNE).

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Linear Discriminant Analysis (LDA).

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Principal Component Analysis (PCA) using the covariance matrix.