MLS-C01 Mock Exam 77 — 25 Questions

Question 1

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a preprocessing step that involves tokenizing text and converting it to a numerical format. To minimize latency, where should the preprocessing logic be implemented?

Accepted Answer

Inside the SageMaker inference container using the inference.py script. To minimize latency, it's best to include the preprocessing logic inside the inference container that serves the model. This avoids additional network calls to separate preprocessing services.

Answer

Using Amazon SageMaker batch transform

Answer

As a separate AWS Lambda function called before the endpoint

Answer

On the client side before sending the request

Question 2

A machine learning engineer is tuning hyperparameters for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is validation accuracy. After several tuning jobs, the best accuracy achieved is 0.85, but the engineer suspects the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

Accepted Answer

Increase the regularization parameter (e.g., lambda or alpha). Option D is correct because increasing the regularization parameter (e.g., lambda or alpha in XGBoost) penalizes model complexity and reduces overfitting. Option A is wrong because increasing learning rate can cause overfitting. Option B is wrong because increasing max depth increases model complexity, leading to overfitting. Option C is wrong because decreasing subsample might reduce overfitting but increasing it introduces more data, which could increase overfitting.

Answer

Increase the maximum depth of trees

Answer

Increase the subsample ratio

Answer

Increase the learning rate

Question 3

A data scientist is training a neural network on Amazon SageMaker and wants to automatically stop training if the validation loss does not improve for 5 consecutive epochs. Which feature should they use?

Accepted Answer

Implement early stopping in the training script. Early stopping with a patience parameter is used to stop training when a metric stops improving. Option A is wrong because Checkpointing saves models, does not stop training. Option B is wrong because Hyperparameter tuning searches for best hyperparameters. Option D is wrong because Debugger monitors training, but early stopping is a built-in feature of the framework.

Answer

SageMaker Debugger

Answer

SageMaker Checkpointing

Answer

SageMaker Hyperparameter Tuning

Question 4

A data scientist is using Amazon SageMaker to train a model. The training job fails with an error 'Insufficient instance capacity'. Which action should the scientist take to resolve this?

Accepted Answer

Retry with a different instance type or Availability Zone. Option D is correct because retrying with a different instance type or in a different Availability Zone often resolves capacity issues. Option A is wrong because the error is about capacity, not permissions. Option B is wrong because reducing data size doesn't affect capacity. Option C is wrong because increasing the instance count may exacerbate the problem.

Answer

Check IAM permissions

Answer

Increase the number of instances

Answer

Reduce the training data size

Question 5

Refer to the exhibit. A training job failed with the error shown. What is the most likely cause?

Accepted Answer

The training data contains missing values or outliers that cause numerical instability. Option A is correct because the error explicitly states input contains NaN or infinity, indicating missing or invalid values in the training data. Option B is wrong because the error is about input values, not about memory. Option C is wrong because the error is from the training script, not from SageMaker capacity. Option D is wrong because the error is about input data, not about model architecture.

Answer

The model architecture is incorrect

Answer

The instance type does not have enough memory

Answer

The training job exceeded the maximum runtime

Question 6

An ML team trains a deep learning model using Amazon SageMaker with a custom Docker container. Training completes successfully, but the model's accuracy on the test set is significantly lower than expected. The team suspects overfitting. Which two actions should they take to mitigate overfitting? (Choose TWO.)

Accepted Answer

Use data augmentation. Dropout and data augmentation are effective regularization techniques to reduce overfitting. Option A (increasing epochs) would worsen overfitting. Option B (reducing batch size) can introduce noise but is not a primary regularization method. Option D (adding more layers) increases model capacity, likely worsening overfitting.

Answer

Reduce the batch size

Answer

Increase the number of layers

Answer

Increase the number of training epochs

Question 7

A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)

Accepted Answer

Real-time endpoints with ml.p3 instance types. Real-time endpoints support GPU instances. Batch transform also supports GPU. Elastic Inference (option C) provides GPU acceleration without a full GPU instance. Option B (Serverless) does not support GPU. Option D (SageMaker Studio) is an IDE, not for inference.

Answer

SageMaker Serverless Inference

Answer

SageMaker Studio

Question 8

A machine learning engineer is deploying a PyTorch model on SageMaker for real-time inference. The model requires GPU for low latency. Which instance type and configuration should the engineer choose?

Accepted Answer

Deploy to an ml.p3.2xlarge instance with a SageMaker endpoint.. SageMaker real-time endpoints support GPU instances like ml.p3.2xlarge. Option A (ml.m5.large) is CPU only. Option B (ml.c5.4xlarge) is CPU. Option D (ml.p3.2xlarge with batch transform) is inference but batch is not real-time; endpoint is needed.

Answer

Deploy to an ml.c5.4xlarge instance with SageMaker batch transform.

Answer

Deploy to an ml.m5.large instance with a SageMaker model endpoint.

Answer

Deploy to an ml.p3.2xlarge instance with SageMaker batch transform.

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. DeepAR is designed for time series forecasting with seasonality and trend. Option A is wrong because XGBoost is a tree-based model for tabular data, not specialized for time series. Option C is wrong because K-Means is clustering. Option D is wrong because Linear Learner can model trends but not seasonality natively.

Answer

Linear Learner

Answer

K-Means

Answer

XGBoost

Question 10

Which THREE of the following are appropriate methods to reduce overfitting in a decision tree model?

Accepted Answer

Prune the tree after training. Option A is correct because pruning reduces tree size. Option B is correct because limiting depth reduces complexity. Option D is correct because setting minimum samples per leaf prevents overfitting. Option C is wrong because increasing depth increases overfitting. Option E is wrong because increasing features may increase overfitting.

Answer

Increase the number of features considered for each split

Answer

Increase the maximum depth of the tree

Question 11

Which TWO of the following are valid techniques for handling missing values in a dataset for machine learning?

Accepted Answer

Remove rows with missing values. Option A is correct because mean imputation is a common technique. Option C is correct because dropping rows with missing values is valid. Option B is wrong because using the maximum value introduces bias. Option D is wrong because adding random noise is not standard. Option E is wrong because converting to string is not appropriate for numerical features.

Answer

Replace missing values with the maximum value of the feature

Answer

Replace missing values with random noise

Answer

Convert missing values to the string 'missing'

Question 12

A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests with varying payload sizes. The company observes occasional latency spikes. Which feature can help mitigate this?

Accepted Answer

Amazon SageMaker Inference Recommender. SageMaker Inference Recommender provides load testing and recommendations for instance type and endpoint configuration. It can help identify optimal settings to reduce latency spikes. Multi-model endpoints are for hosting multiple models, not directly for latency spikes. Elastic Inference is for accelerating deep learning inference, not general latency. Automatic scaling adjusts capacity but not per-request latency.

Answer

Multi-model endpoints

Answer

Amazon Elastic Inference

Answer

Automatic scaling

Question 13

Which TWO of the following are appropriate use cases for Amazon SageMaker built-in algorithms?

Accepted Answer

Classifying customer churn using tabular data. XGBoost is suitable for tabular classification. BlazingText is for text classification on word embeddings. Image classification using custom CNNs may use built-in but not necessarily. Time series forecasting is not a built-in algorithm (use DeepAR). Reinforcement learning is not a built-in algorithm.

Answer

Reinforcement learning using Q-learning

Answer

Image classification using a custom CNN architecture

Answer

Time series forecasting using ARIMA

Question 14

A machine learning engineer is training a model using Amazon SageMaker. The training data is stored in S3 and is 10 TB. The engineer wants to use Pipe input mode to stream data from S3. Which algorithm supports Pipe mode?

Accepted Answer

Amazon SageMaker XGBoost. Option A is correct because Amazon SageMaker XGBoost supports Pipe mode for streaming. Option B is wrong because Linear Learner does not support Pipe mode. Option C is wrong because K-Means does not support Pipe mode. Option D is wrong because PCA does not support Pipe mode.

Answer

Amazon SageMaker Linear Learner

Answer

Amazon SageMaker K-Means

Answer

Amazon SageMaker PCA

Question 15

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 the user. What is the likely issue?

Accepted Answer

The policy is missing s3:ListBucket permission. Option C is correct because the policy allows s3:GetObject and s3:PutObject on the bucket, but the training job also needs s3:ListBucket to read the objects. Option A is wrong because the policy has sagemaker:CreateTrainingJob on all resources. Option B is wrong because the policy allows sagemaker actions. Option D is wrong because the policy allows s3:PutObject, but the error is about access denied, not about upload.

Answer

The policy does not allow sagemaker:CreateTrainingJob

Answer

The policy does not allow sagemaker:CreateModel

Answer

The policy does not allow s3:PutObject

Question 16

A data scientist is using Amazon SageMaker to train a linear regression model. The training job fails with the error: 'AlgorithmError: Input data has NaN values'. Which step should the data scientist take to resolve this issue?

Accepted Answer

Impute missing values or remove rows with NaN values. The error indicates NaN values in the input data. The correct action is to handle missing values before training. Option A is wrong because increasing instance count does not fix data issues. Option C is wrong because the error is data-related, not algorithm-related. Option D is wrong because the issue is NaN values, not data format.

Answer

Convert the data to a sparse format

Answer

Switch to a different algorithm that handles missing values

Answer

Increase the number of training instances

Question 17

A machine learning team is developing a model to predict housing prices. They have a dataset with numerical features like square footage and number of bedrooms, and categorical features like neighborhood. Which preprocessing step is essential before training a linear regression model?

Accepted Answer

One-hot encode categorical features. One-hot encoding converts categorical features into binary columns, which linear regression requires. Option A is wrong because scaling is important but not the only essential step; encoding is needed first. Option B is wrong because PCA reduces dimensionality but is optional. Option D is wrong because feature selection is not essential for all models.

Answer

Normalize all numerical features to have zero mean and unit variance

Answer

Remove highly correlated features

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality

Question 18

A data scientist wants to build a binary classifier to predict customer churn. The dataset has 10,000 records with 500 churners (5%). Which technique should the data scientist use to address class imbalance?

Accepted Answer

Use SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic samples.. SMOTE generates synthetic samples for the minority class, which is appropriate for imbalanced datasets. Option A (downsampling majority class) would lose data. Option B (upweighting minority class) is possible but less common. Option D (random undersampling) also loses data.

Answer

Randomly undersample the majority class.

Answer

Assign higher class weights to the minority class.

Answer

Downsample the majority class to match the minority class size.

Question 19

A company is training a large language model on Amazon SageMaker using a single GPU instance. The training is taking too long. Which change would most likely reduce training time?

Accepted Answer

Use a larger instance with multiple GPUs and enable distributed training. Using multiple GPUs in a distributed training job can parallelize work and reduce time. Option B is wrong because decreasing batch size often increases training time due to more updates. Option C is wrong because storing data in S3 Standard vs Glacier Access does not affect training speed. Option D is wrong because increasing instance memory may not help if the bottleneck is compute.

Answer

Increase the instance memory

Answer

Decrease the batch size

Answer

Store the training data in S3 Glacier for faster access

Question 20

A team is building a model to predict customer churn. They have 50 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). Which feature engineering technique is most appropriate?

Accepted Answer

Target encoding. Target encoding replaces each category with the mean of the target variable, which handles high cardinality well. Option A is wrong because one-hot encoding would create 10,000 binary columns, causing high dimensionality. Option B is wrong because label encoding implies ordinality. Option D is wrong because binning reduces cardinality but loses information.

Answer

Binning zip codes into regions

Answer

Label encoding

Answer

One-hot encoding