MLS-C01 Mock Exam 85 — 25 Questions

Question 1

A data scientist is building a time series forecasting model for monthly sales data. The scientist has observed that the sales data shows a clear upward trend and a seasonal pattern that repeats every 12 months. Which algorithm would be most appropriate for this task?

Accepted Answer

ARIMA. ARIMA (Autoregressive Integrated Moving Average) is specifically designed for time series forecasting and can handle both trend and seasonality through its parameters: the 'I' (differencing) removes trend, and seasonal ARIMA (SARIMA) extends it with seasonal differencing and seasonal AR/MA terms to capture the 12-month repeating pattern. This makes it the most appropriate choice for monthly sales data with a clear upward trend and annual seasonality.

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Random Forest

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k-means clustering

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Linear regression with time-based features

Question 2

A data scientist is training a linear regression model and notices high bias in the training set. What action is most likely to reduce bias?

Accepted Answer

Add more relevant features to the model.. High bias indicates that the model is underfitting the training data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases the model's capacity to learn complex relationships, directly reducing bias. This is a standard approach in linear regression to address underfitting.

Answer

Apply L1 regularization.

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Increase the learning rate.

Answer

Increase the amount of training data.

Question 3

Which TWO of the following are examples of unsupervised learning tasks?

Accepted Answer

Dimensionality reduction using PCA. Principal Component Analysis (PCA) is an unsupervised learning technique used for dimensionality reduction. It works by identifying the directions (principal components) that maximize variance in the data, without requiring any labeled target variable. This makes it a classic example of unsupervised learning, as the algorithm learns patterns solely from the input features.

Answer

Classifying emails as spam or not spam

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Sentiment analysis of product reviews

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Predicting house prices

Question 4

A CloudFormation stack creation failed. The SageMaker endpoint resource shows CREATE_FAILED. What is the most likely issue?

Accepted Answer

The S3 bucket 'my-bucket' does not contain the object 'model.tar.gz'.. The correct answer is B because a CREATE_FAILED status on a SageMaker endpoint resource during CloudFormation stack creation most commonly indicates that the model artifact specified in the Model definition cannot be located. SageMaker requires the S3 bucket and object path (e.g., 's3://my-bucket/model.tar.gz') to exist and be accessible at the time of model creation. If the object is missing, the model resource fails, cascading to the endpoint creation failure.

Answer

The IAM role used by CloudFormation lacks permissions to create endpoints.

Answer

The SageMaker endpoint configuration is invalid.

Answer

The instance type specified for the endpoint is not available in the region.

Question 5

A machine learning engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take to potentially improve the result?

Accepted Answer

Increase the total number of training jobs to 50.. Option C is correct because increasing the total number of training jobs from 20 to 50 gives the Bayesian optimization algorithm more opportunities to explore the hyperparameter space and exploit promising regions. With only 10 jobs completed, the tuning job may not have converged to the global minimum of MAE, and additional jobs can refine the search, especially since Bayesian search builds a probabilistic model that improves with more observations.

Answer

Set early stopping to avoid overfitting.

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Change the objective metric to RMSE.

Answer

Switch the tuning strategy from Bayesian to Random search.

Question 6

A data scientist is training a model using SageMaker's built-in XGBoost algorithm with a large dataset stored in CSV format. The training job is using File mode. The data scientist wants to reduce the time it takes to start training. Which approach would be most effective?

Accepted Answer

Use Pipe mode for the input data channel.. Pipe mode streams data directly from Amazon S3 into the training container, eliminating the need to first download the entire dataset to the EBS volume. This reduces the startup time significantly because training can begin as soon as the first records arrive, rather than waiting for the full download to complete.

Answer

Increase the size of the EBS volume.

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Convert the data to Parquet format.

Answer

Increase the number of training instances.

Question 7

A company has a time series forecasting problem with daily sales data. The data shows both trend and seasonality. Which Amazon SageMaker built-in algorithm is most suitable?

Accepted Answer

DeepAR. DeepAR is a supervised learning algorithm for time series forecasting that explicitly models both trend and seasonality using recurrent neural networks (RNNs). It is designed to handle multiple related time series, incorporate additional features like holidays or promotions, and produce probabilistic forecasts, making it the most suitable choice for daily sales data with trend and seasonal patterns.

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K-Means

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Linear Learner

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XGBoost

Question 8

A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The data scientist trains a gradient boosting model and deploys it to a SageMaker endpoint with a single ml.c5.xlarge instance. During load testing, the endpoint exceeds the latency threshold. Which change is MOST likely to reduce latency?

Accepted Answer

Replace the model with a simpler model, such as logistic regression. Option A is correct because replacing the gradient boosting model with a simpler model like logistic regression reduces the computational complexity per inference. Gradient boosting involves traversing many decision trees, each requiring multiple conditional checks and arithmetic operations, while logistic regression is a single linear transformation. This directly lowers CPU utilization per request, reducing latency under the same instance resources.

Answer

Use a larger instance type, such as ml.c5.4xlarge

Answer

Switch to batch transform for inference

Answer

Enable automatic scaling on the endpoint

Question 9

Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to their role. What is the most likely reason for the error?

Accepted Answer

The policy only allows CreateTrainingJob when the training job status is 'Failed', which is never true initially. Option A is correct because the IAM policy uses a `Condition` block with `sagemaker:TrainingJobStatus` set to `Failed`. When a `CreateTrainingJob` API call is made, the training job status is not yet set (it is `Creating` or `InProgress`), so the condition evaluates to false, and the request is denied. The policy only grants permission when the status equals `Failed`, which never occurs at creation time.

Answer

The Action is not allowed because 'CreateTrainingJob' is misspelled

Answer

There is an explicit deny in another policy

Answer

The Resource is set to '*' which does not include the specific training job ARN

Question 10

A data scientist is training a binary classification model on an imbalanced dataset where the positive class is rare. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the data scientist prioritize to improve model performance?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.

Answer

AUC-ROC

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Precision

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Accuracy

Question 11

Which THREE of the following are best practices for training deep learning models on Amazon SageMaker?

Accepted Answer

Use SageMaker Debugger to profile system bottlenecks. SageMaker Debugger is a best practice because it provides real-time profiling of system bottlenecks such as CPU/GPU utilization, memory I/O, and network throughput during training. This allows you to identify and resolve performance issues early, optimizing training efficiency and cost. It integrates directly with SageMaker's training jobs without requiring code changes.

Answer

Disable automatic scaling to avoid interruptions

Answer

Always use the largest instance type available for faster training

Question 12

A data scientist is analyzing a time-series dataset and wants to check for stationarity. Which EDA technique is most appropriate?

Accepted Answer

Perform the Augmented Dickey-Fuller (ADF) test.. The Augmented Dickey-Fuller (ADF) test is a formal statistical hypothesis test specifically designed to check for stationarity in a time series. It tests the null hypothesis that a unit root is present, indicating non-stationarity, against the alternative of stationarity. This makes it the most appropriate EDA technique for directly assessing stationarity.

Answer

Plot the autocorrelation function (ACF).

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Use time-series cross-validation.

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Create a scatter plot of the series against its lag.

Question 13

A data scientist is using SageMaker to train a deep learning model with a large dataset stored in S3. The training is taking a long time. Which action would most likely reduce training time without sacrificing accuracy?

Accepted Answer

Use SageMaker Pipe Input mode. SageMaker Pipe Input mode streams training data directly from S3 into the algorithm without first downloading it to the local EBS volume. This eliminates the I/O bottleneck caused by large dataset downloads, significantly reducing training time while preserving accuracy because the model sees the same data.

Answer

Increase the batch size

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Use a smaller instance type

Answer

Reduce the number of epochs

Question 14

A data scientist is exploring a dataset of customer transactions. The dataset has 1 million rows and 50 columns. The target variable is a binary flag indicating whether a customer churned. The data scientist runs a correlation matrix on all numerical features and finds that two features have a correlation coefficient of 0.98. Which action should be taken to improve model performance?

Accepted Answer

Remove one of the two highly correlated features from the dataset.. Two features with a correlation coefficient of 0.98 are nearly perfectly multicollinear. This inflates the variance of coefficient estimates in linear models, making them unstable and reducing interpretability. Removing one of the highly correlated features is a standard dimensionality reduction technique that mitigates multicollinearity without significant information loss, as the remaining feature captures almost the same variance.

Answer

Create an interaction term between the two features.

Answer

Increase the regularization parameter (e.g., lambda) in the model.

Answer

Apply mean-centering to both features to reduce correlation.

Question 15

During exploratory data analysis, a data scientist plots the distribution of a numerical feature and observes a heavy right skew. The feature has many outliers at the high end. Which transformation is most appropriate to reduce skewness?

Accepted Answer

Apply a log transformation to the feature.. A log transformation compresses the range of the data, reducing the impact of extreme values and pulling in the long tail of a right-skewed distribution. This makes the feature more normally distributed, which is often required for linear models and many statistical tests. It is the standard technique for handling positive-valued features with heavy right skew.

Answer

Apply z-score normalization.

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Apply one-hot encoding.

Answer

Apply min-max scaling.

Question 16

A team is building a regression model to predict house prices. The dataset includes a column 'zip_code' with 100 unique values. The data scientist one-hot encodes this column, resulting in 100 new binary columns. The model shows poor performance on a validation set. What is the most likely cause?

Accepted Answer

The model suffers from the curse of dimensionality due to the large number of features.. One-hot encoding 'zip_code' with 100 unique values creates 100 binary features. With only 100 features, the dataset is not high-dimensional enough to cause the curse of dimensionality, which typically requires thousands of features. The poor performance is more likely due to other issues like overfitting or data leakage, not the curse of dimensionality. Option D is incorrect because the curse of dimensionality is not the most likely cause in this scenario.

Answer

One-hot encoding introduced multicollinearity among the binary columns.

Answer

One-hot encoding reduced the number of features, causing underfitting.

Answer

The one-hot encoding introduced high variance, but the validation set has low variance.

Question 17

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.. Option C is correct because 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.

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Drop the entire feature from the dataset.

Question 18

A data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?

Accepted Answer

Use GPU instances instead of CPU instances. GPU instances (e.g., P3, P4d) are optimized for the massively parallel matrix operations required by deep neural networks, providing orders-of-magnitude faster computation than CPU instances for training tasks. By offloading tensor operations to GPU cores, the training time is significantly reduced, especially for large models and datasets.

Answer

Increase the batch size

Answer

Use a smaller instance type

Question 19

A company is using SageMaker to deploy a model for real-time inference. The model requires GPU for low latency. Which THREE configurations should the company consider for high availability and cost optimization? (Choose three.)

Accepted Answer

Use a multi-model endpoint to share GPU instances among multiple models.. Option B is correct because a multi-model endpoint allows multiple models to be hosted on the same GPU-backed instance, sharing the GPU resources and reducing idle time. This improves cost efficiency by maximizing GPU utilization while still providing low-latency inference for each model. It is a recommended pattern for serving many models with GPU requirements without provisioning separate endpoints.

Answer

Use Spot instances for the endpoint.

Answer

Use SageMaker Batch Transform for inference.

Question 20

A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist wants to speed up training by using distributed training across multiple GPUs. Which SageMaker feature or configuration should the data scientist use?

Accepted Answer

SageMaker Data Parallelism library. Option D is correct because the SageMaker Data Parallelism library is specifically designed to distribute training across multiple GPUs by splitting the input data across workers, which reduces per-GPU computation time and accelerates training for deep learning models. This library uses optimized all-reduce algorithms (e.g., Ring AllReduce) to synchronize gradients efficiently, making it ideal for speeding up image classification tasks that are data-intensive.

Answer

SageMaker Debugger

Answer

Model parallelism in SageMaker

Answer

SageMaker hyperparameter tuning