MLS-C01 Practice Test 20 — 15 Questions

Question 1

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 designed for implicit feedback datasets in collaborative filtering. Option A is wrong because Linear Regression is for supervised regression, not recommendation. Option C is wrong because K-means is clustering, not recommendation. Option D is wrong because SVD is typically used for explicit ratings, while ALS is better suited for implicit feedback.

Answer

Linear Regression

Answer

K-means clustering

Answer

Singular Value Decomposition (SVD)

Question 2

Refer to the exhibit. A custom training job using Pipe input mode fails. The logs indicate the algorithm cannot read the data. What is the most likely issue?

Accepted Answer

The algorithm expects File mode but Pipe mode is specified. Pipe mode streams data from S3 via stdin, but the algorithm must be designed to read from a pipe rather than a file. Many custom algorithms expect file input, causing a failure. Option A is correct because Pipe mode is incompatible with algorithms expecting File mode. Option B is incorrect because instance size does not affect data reading. Option C is incorrect because the data is not compressed. Option D is incorrect because the training image is accessible.

Answer

The instance type is too small for the data

Answer

The training data is compressed

Answer

The training image is not accessible

Question 3

A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 deep learning models, each 500 MB. The total model size is 5 GB, which exceeds the 5 GB limit for SageMaker real-time endpoints. The data scientist wants to reduce the model size without significantly impacting accuracy. The ensemble uses averaging of predictions from all models. The scientist has access to a validation set with 10,000 samples. Which technique should the scientist use to reduce the model size?

Accepted Answer

Use model distillation to train a smaller model that approximates the ensemble. Model distillation trains a smaller student model to mimic the ensemble, reducing size while preserving accuracy. Option B is wrong because price-aware instance selection does not reduce model size. Option C is wrong because SageMaker Neo is for optimization, not size reduction below 5 GB. Option D is wrong because pruning alone may not reduce size enough.

Answer

Use a more expensive instance type to host the model

Answer

Use SageMaker Neo to compile and optimize the model

Answer

Apply weight pruning to each model in the ensemble

Question 4

A data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. After the Autopilot job completes, the best model has an accuracy of 0.85 on the validation set. However, the data scientist notices a class imbalance (90% negative, 10% positive). Which metric should the data scientist use to evaluate the model's performance on the positive class?

Accepted Answer

Area Under the ROC Curve (AUC). A is correct because Area Under the ROC Curve (AUC) is threshold-independent and evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds. In the presence of severe class imbalance (90% negative, 10% positive), AUC provides a robust measure of model performance on the positive class without being skewed by the majority class, unlike accuracy which would be high even if the model predicts all negatives.

Answer

Recall

Answer

Accuracy

Answer

Precision

Question 5

A machine learning engineer is exploring a dataset with 500 features and 10,000 samples. To reduce dimensionality for visualization, which technique is most suitable if the goal is to preserve global data structure?

Accepted Answer

Principal Component Analysis (PCA). PCA is the most suitable technique for preserving the global data structure when reducing dimensionality because it is a linear method that maximizes variance along orthogonal principal components, capturing the overall covariance structure of the 500 features. Unlike nonlinear methods, PCA ensures that the global relationships (e.g., distances between clusters) are retained, making it ideal for visualization of high-dimensional data where the goal is to see broad patterns.

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Locally Linear Embedding (LLE)

Answer

Uniform Manifold Approximation and Projection (UMAP)

Question 6

A company wants to use Amazon SageMaker to train a deep learning model using a custom TensorFlow script. The data is stored in an S3 bucket. Which SageMaker API operation should be used to launch the training job?

Accepted Answer

CreateTrainingJob. The correct API operation to launch a training job in Amazon SageMaker is CreateTrainingJob. This operation specifies the training algorithm (or custom script), resource configuration (instance type and count), input data configuration (pointing to S3), and output location for the model artifacts. It directly initiates the training process on SageMaker-managed infrastructure.

Answer

CreateHyperParameterTuningJob

Answer

CreateEndpoint

Answer

CreateTransformJob

Question 7

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?

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

Accepted Answer

RecordIO-protobuf with user_id, item_id, and label fields. The built-in Factorization Machines algorithm in Amazon SageMaker requires the RecordIO-protobuf format for optimal performance, as it allows efficient binary serialization and direct integration with SageMaker's distributed training infrastructure. This format supports sparse data representation, which is critical for high-dimensional user-item interaction data, and enables faster I/O and reduced memory overhead compared to text-based formats.

Answer

Libsvm format with user_id and item_id as features

Answer

CSV file with user_id, item_id, and label columns

Answer

JSON lines file with user_id, item_id, and label fields

Question 8

A data scientist is performing hyperparameter optimization for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is 'validation:logloss'. Which TWO strategies can help the tuning job converge faster? (Choose TWO.)

Accepted Answer

Use Bayesian optimization strategy. Bayesian optimization is a hyperparameter tuning strategy that builds a probabilistic model of the objective function and uses it to select the most promising hyperparameter combinations to evaluate next. By focusing on regions of the hyperparameter space that are likely to yield better validation:logloss, it converges to an optimal configuration in fewer training jobs compared to uninformed search methods, thus speeding up the tuning process.

Answer

Increase the number of tuning jobs

Answer

Increase the resource limits for each training job

Answer

Use random search strategy

Question 9

A research institution is building a data lake to store genomics data. Each experiment generates multiple files totaling about 500 GB. The data is stored in Amazon S3 and needs to be processed by multiple machine learning (ML) training jobs running on Amazon SageMaker. The data has a high churn rate; after 30 days, most data becomes irrelevant and should be moved to Amazon S3 Glacier Deep Archive. The institution wants to minimize storage costs while maintaining data durability. Which S3 storage class should they use for the first 30 days?

Accepted Answer

Use S3 Intelligent-Tiering for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.. S3 Intelligent-Tiering is the most cost-effective storage class for the first 30 days because it automatically moves data between frequent and infrequent access tiers based on usage, without any retrieval fees. This is ideal for genomics data that may have unknown or changing access patterns during the initial processing period. After 30 days, a lifecycle rule transitions the data to S3 Glacier Deep Archive for long-term storage, minimizing costs. S3 Standard is more expensive for data that may not be accessed frequently, S3 One Zone-IA lacks durability across Availability Zones, and S3 Glacier Instant Retrieval is designed for long-lived, rarely accessed data and is not cost-effective for the first 30 days.

Answer

Use S3 One Zone-IA for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.

Answer

Use S3 Standard for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.

Answer

Use S3 Glacier Instant Retrieval for all data, and set a lifecycle policy to transition to S3 Glacier Deep Archive after 30 days.

Question 10

A machine learning engineer is working on a regression problem to predict house prices. The dataset contains 500,000 rows and 20 features, including 'sqft_living', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', 'yr_built', 'zipcode', and 'lat'. After performing exploratory data analysis, the engineer notices that the 'sqft_living' feature has a right-skewed distribution with a long tail. The 'zipcode' feature is categorical with 70 unique values. The 'lat' feature is continuous. The engineer wants to prepare the data for a linear regression model. Which action should the engineer take to improve model performance?

Accepted Answer

Apply a log transformation to the 'sqft_living' feature.. Linear regression assumes that features are approximately normally distributed, and a right-skewed distribution like 'sqft_living' can violate this assumption, leading to poor model performance. Applying a log transformation compresses the long tail, making the distribution more symmetric and helping the model learn a linear relationship between the feature and the target. This is a standard preprocessing step for skewed features in regression tasks.

Answer

Remove the 'sqft_living' feature because it violates the normality assumption.

Answer

One-hot encode the 'zipcode' feature to capture location effects.

Answer

Apply standard scaling (z-score) to the 'sqft_living' feature.

Question 11

Which TWO actions can help reduce overfitting in a decision tree model? (Choose 2.)

Accepted Answer

Prune the tree after training. Pruning the tree after training removes branches that have little predictive power, reducing overfitting by simplifying the model. This technique directly addresses the variance component of the bias-variance tradeoff, making the model generalize better to unseen data.

Answer

Increase the maximum depth of the tree

Answer

Increase the number of features considered at each split

Answer

Use all training data without validation

Question 12

A data scientist is analyzing a dataset with missing values in 30% of the rows for the 'age' column. The data scientist decides to impute the missing values with the median of the observed 'age' values. What is a potential drawback of this approach?

Accepted Answer

The imputed values may reduce the variance of the 'age' distribution.. Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.

Answer

The imputation will introduce bias if the missing values are not random.

Answer

Imputation using median is computationally expensive for large datasets.

Answer

The imputed values will increase the variance of the feature, leading to overfitting.

Question 13

A data scientist notices that a linear regression model trained on a dataset has high variance. The model performs well on the training data but poorly on the test data. Which action is most likely to reduce the variance?

Accepted Answer

Apply L2 regularization to the model. High variance indicates the model is overfitting to the training data. L2 regularization (ridge regression) adds a penalty proportional to the square of the magnitude of the coefficients, which shrinks them toward zero. This reduces the model's sensitivity to noise in the training data, thereby lowering variance and improving generalization to the test set.

Answer

Decrease the amount of training data

Answer

Increase the number of gradient descent iterations

Answer

Add more features to the model

Question 14

Refer to the exhibit. What is the recall of the model?

Accepted Answer

0.80. Recall is calculated as True Positives divided by the sum of True Positives and False Negatives. From the confusion matrix, True Positives = 80 and False Negatives = 20, so recall = 80 / (80 + 20) = 0.80. Option B is correct.

Answer

0.85

Answer

0.89

Answer

0.90

Question 15

Refer to the exhibit. A data scientist ran an S3 Select query on a large CSV file stored in Amazon S3. The output shows only 2 records returned, but the data scientist expected thousands. The file size is 10 GB. What is the MOST likely reason for the small result set?

Accepted Answer

The city column may have leading/trailing spaces or case differences.. S3 Select performs exact string matching by default, so if the WHERE clause filters on the city column, any leading/trailing spaces or case differences will cause mismatches, returning far fewer rows than expected. The query likely used a literal like 'New York' while the data contains ' New York ' or 'new york', resulting in only 2 matches instead of thousands.

Answer

The file needs to be indexed by S3 Select before querying.

Answer

The CSV file contains nested arrays that S3 Select cannot parse.

Answer

S3 Select does not support the WHERE clause on CSV files.