MLS-C01 Modeling Practice Test 23 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features, and the scientist wants to interpret the model's coefficients. Which algorithm should they use?

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

Accepted Answer

Amazon SageMaker Linear Learner. Amazon SageMaker Linear Learner provides interpretable coefficients, which is essential for understanding the impact of each feature in a linear regression model. Option A is wrong because XGBoost is a tree-based ensemble method that is less interpretable and does not provide linear coefficients. Option B is wrong because K-Means is an unsupervised clustering algorithm, not suited for regression. Option C is wrong because Factorization Machines are designed for high-dimensional sparse data and are not the standard choice for linear regression with 10 features.

Answer

Amazon SageMaker XGBoost

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Amazon SageMaker K-Means

Answer

Amazon SageMaker Factorization Machines

Question 2

A data scientist is training an LSTM model for time series forecasting using Amazon SageMaker. The model is overfitting. Which action is LEAST likely to reduce overfitting?

Accepted Answer

Increase the number of LSTM layers. Increasing the number of LSTM layers increases model capacity, which makes the network more expressive and therefore more prone to memorizing the training data — the opposite of what you want when overfitting. The other three options (dropout, fewer hidden units, early stopping) all reduce effective capacity or regularize training. In an overfitting scenario, adding depth without additional regularization typically worsens the train/validation gap.

Answer

Add dropout layers

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Reduce the number of hidden units

Answer

Use early stopping

Question 3

A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires 500 MB of memory and has a latency requirement of 100 ms. The endpoint is receiving 10 requests per second. Which instance type should be chosen for cost-effectiveness?

Accepted Answer

ml.m5.large. The ml.m5.large instance type provides a balance of compute, memory, and cost for this workload. It has 8 GiB of memory, which is more than enough for the 500 MB model, and sufficient vCPU to handle 10 requests per second with low latency. It is more cost-effective than the other options while meeting the requirements.

Answer

ml.c5.xlarge

Answer

ml.t2.medium

Answer

ml.p3.2xlarge

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. Retrying with a different instance type or in a different Availability Zone often resolves capacity issues. Option A is incorrect because the error is about capacity, not IAM permissions. Option C is incorrect because increasing the number of instances may exacerbate the capacity issue. Option D is incorrect because reducing the training data size does not affect instance capacity.

Answer

Check IAM permissions

Answer

Increase the number of instances

Answer

Reduce the training data size

Question 5

A company is using Amazon SageMaker to deploy a model for real-time inference. The model is a deep neural network that requires GPU for low latency. The endpoint currently uses a single ml.p3.2xlarge instance. Traffic is expected to increase by 5x. Which TWO actions should the company take to handle the increased traffic?

Accepted Answer

Use a larger instance type with more GPUs. Option A is correct because moving to a larger GPU instance type (e.g., ml.p3.8xlarge or ml.p3.16xlarge, which have 4 or 8 NVIDIA V100 GPUs respectively) increases per-instance compute and memory capacity, allowing each endpoint instance to serve more concurrent inference requests with the low latency the deep neural network requires. Option C is correct because SageMaker supports automatic scaling of real-time endpoints via Application Auto Scaling, which adjusts the number of instances based on metrics such as InvocationsPerInstance or ModelLatency, so the endpoint can elastically add instances to absorb the 5x traffic increase. Option B is not appropriate because switching to a CPU-based instance would remove the GPU acceleration the model needs for low latency. Option D is not appropriate because multi-model endpoints are designed to host many models behind a single endpoint to save hosting costs, not to scale throughput for one latency-sensitive GPU model. Option E is not appropriate because decreasing the batch size reduces per-request work but does not add capacity, and it can actually lower GPU utilization and throughput.

Answer

Switch to a CPU-based instance

Answer

Use a multi-model endpoint

Answer

Decrease the batch size

Question 6

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. The model achieves 99% accuracy but only identifies 10% of the actual positive cases. Which metric should the data scientist focus on to evaluate the model's performance on the positive class?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases the model correctly identifies — exactly the metric that is failing here at 10%. With a 5% positive class, accuracy is misleading because predicting 'negative' for everything yields 95% accuracy. Recall directly quantifies how many true positives are being missed.

Answer

Precision

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AUC-ROC

Answer

F1 score

Question 7

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. Option A (Use data augmentation) is correct because expanding the training set with transformed copies (rotations, flips, crops, noise) increases data diversity and reduces the model's ability to memorize the training samples, which directly combats overfitting. Option B (Add dropout layers) is correct because dropout randomly deactivates a fraction of neurons during training, forcing the network to learn more robust, distributed features instead of relying on specific neurons, a standard regularization technique against overfitting. Option C (Reduce the batch size) is not a recognized overfitting remedy; smaller batches mainly affect gradient noise and convergence speed, and can even increase variance. Option D (Increase the number of layers) would raise model capacity and typically worsen overfitting. Option E (Increase the number of training epochs) lets the model fit the training data even more closely, further aggravating overfitting.

Answer

Reduce the batch size

Answer

Increase the number of layers

Answer

Increase the number of training epochs

Question 8

A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features, and the scientist wants to interpret the model's coefficients. Which algorithm should they use?

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

Accepted Answer

Impute missing values or remove rows with NaN values. The error 'AlgorithmError: Input data has NaN values' indicates missing values in the dataset. Linear regression cannot handle NaN values. The appropriate action is to either impute missing values (e.g., using mean/median) or remove rows with NaN values. Option A (convert to sparse format) is used for handling zero values, not missing values, and will not resolve NaN errors. Option B (switch to a different algorithm) is unnecessary because data preprocessing can fix the issue; moreover, many algorithms also cannot handle NaN. Option C is correct. Option D (increase training instances) only adds more compute resources and does not fix data quality issues.

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 9

A company is building a recommendation system using collaborative filtering. The dataset contains implicit feedback (clicks) from users on items. Which algorithm is best suited for this scenario?

Accepted Answer

Alternating Least Squares (ALS). Alternating Least Squares (ALS) is specifically designed for collaborative filtering with implicit feedback data, such as clicks. It handles the implicit nature by treating observed interactions as confidence weights and optimizes the latent factors efficiently. ALS is widely used in recommendation systems for its scalability and ability to handle large sparse matrices.

Answer

Linear Regression

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K-means clustering

Answer

Singular Value Decomposition (SVD)

Question 10

A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The model is overfitting on the training data. Which hyperparameter adjustment is most likely to reduce overfitting?

Accepted Answer

Increase lambda (L2 regularization). Increasing lambda (L2 regularization) penalizes large leaf weights in XGBoost, shrinking the model's complexity and reducing its ability to fit noise in the training data, which directly combats overfitting. L2 regularization is one of the primary levers in the XGBoost objective function alongside alpha (L1) and gamma (minimum loss reduction). Raising lambda is a standard, low-risk adjustment when a boosted model overfits.

Answer

Increase learning rate (eta)

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Increase max_depth

Answer

Increase num_round

Question 11

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. Linear regression requires numerical inputs, so categorical features like neighborhood must be converted into a numerical representation before training. One-hot encoding creates binary indicator columns for each category, allowing the model to use the information without imposing a false ordinal relationship. This is an essential preprocessing step for categorical variables in linear regression.

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 12

A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features, and the scientist wants to interpret the model's coefficients. Which algorithm should they use?

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

Accepted Answer

Use Pipe mode for data input. Pipe mode streams training data directly from Amazon S3 into the training container as a Linux FIFO pipe, eliminating the need to download the full dataset to the instance's EBS volume before training begins. This dramatically reduces startup time and improves I/O throughput for large datasets, which is exactly the bottleneck described. Since the model architecture is unchanged, Pipe mode is the correct lever to pull for faster training.

Answer

Use a smaller instance type

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Increase the number of epochs

Answer

Decrease the batch size

Question 13

A data scientist is building a text classification model using a bag-of-words approach. The dataset contains 100,000 documents with a vocabulary of 50,000 unique words. The model is overfitting. Which THREE techniques can help reduce overfitting? (Choose THREE.)

Accepted Answer

Apply L1 or L2 regularization. Option B is correct because adding L1 or L2 regularization to the classifier penalizes large coefficients, shrinking the model's capacity and directly combating the high variance that causes overfitting on a 100,000-document bag-of-words matrix. Option C is correct because restricting the n-gram range to unigrams only removes higher-order bigram/trigram features, drastically cutting the feature space and reducing the model's ability to memorize spurious word co-occurrence patterns in the training set. Option D is correct because feature selection that removes rare words (e.g., dropping terms below a min_df threshold or using chi-squared/SelectKBest) eliminates noisy, low-frequency tokens that appear in few documents and are prime candidates for overfitting. Option A is not correct because increasing max_features expands the vocabulary and adds more parameters, which typically worsens overfitting rather than reducing it. Option E is not correct because switching from raw counts to TF-IDF is a weighting/normalization change that can improve performance but does not by itself constrain model complexity or reduce overfitting.

Answer

Increase max_features to include more words

Answer

Use TF-IDF instead of raw counts

Question 14

A company is training a deep learning model on SageMaker using a large dataset stored in S3. The training job is taking a long time due to I/O bottlenecks. Which action would MOST effectively reduce the I/O bottleneck?

Accepted Answer

Use Pipe input mode in the SageMaker estimator.. SageMaker Pipe input mode streams training data directly from S3 to the training container over a Unix domain socket, avoiding the full dataset download to local EBS storage. This dramatically reduces startup time and I/O bottlenecks for large datasets, making it the most effective single action.

Answer

Use Amazon EFS as the data source.

Answer

Use Amazon FSx for Lustre as the data source.

Answer

Increase the number of training instances.

Question 15

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.. The ml.p3.2xlarge instance type includes a GPU (NVIDIA Tesla V100), which is necessary for low-latency GPU inference. Deploying with a SageMaker endpoint provides a real-time inference endpoint suitable for low-latency requirements. Batch transform is for offline, batch processing and does not provide real-time low latency.

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.