MLS-C01 Practice Test 28 — 15 Questions

Question 1

A data scientist is performing EDA on a dataset containing text reviews. To understand the most common words, the data scientist generates a word cloud. Which preprocessing step is most important to ensure the word cloud reflects meaningful content?

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

Accepted Answer

Stop word removal. Removing stop words (common words like 'the', 'and') ensures that the word cloud highlights meaningful content. Stemming (C) may not be necessary for a word cloud. Tokenization (D) is fundamental but not the most critical for meaningfulness. POS tagging (B) is overkill.

Answer

Part-of-speech tagging

Answer

Stemming

Answer

Tokenization

Question 2

A machine learning engineer is analyzing feature distributions in a dataset and notices that one feature has a long tail. Which transformation is most appropriate to reduce skewness and make the distribution more normal?

Accepted Answer

Apply a log transformation. Log transformation is the most appropriate technique to reduce right skewness (long tail) and make the distribution closer to normal. One-hot encoding is used for categorical variables, not for transforming skewed numerical features. Min-max normalization scales features to a range but does not change the shape of the distribution. Standardization (Z-score) centers the data and scales by standard deviation, but also does not reduce skewness.

Answer

Apply one-hot encoding

Answer

Apply min-max normalization

Answer

Apply standardization (Z-score)

Question 3

Which THREE of the following are common causes of multicollinearity in a linear regression model?

Accepted Answer

Including a polynomial term (e.g., x^2) along with the original variable. Options A, C, and D are correct. Dummy variable trap occurs when all categories are included without dropping one. Highly correlated predictors directly cause multicollinearity. Including polynomial terms creates correlation with the original variable. B (interaction terms) can also cause but is less common. E (outliers) does not cause multicollinearity.

Answer

Including interaction terms between independent variables

Answer

Presence of outliers in the target variable

Question 4

A data analyst wants to understand the distribution of a continuous variable. Which visualization is most appropriate for this purpose?

Accepted Answer

Histogram. (Histogram) is correct because a histogram displays the frequency distribution of a continuous variable by grouping data into bins. Option A (Box plot) is incorrect because it shows summary statistics (median, quartiles, outliers) but not the full distribution. Option B (Bar chart) is incorrect because bar charts are for categorical data, not continuous. Option D (Scatter plot) is incorrect because it shows the relationship between two continuous variables, not the distribution of a single variable.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 5

A data scientist is analyzing a dataset with a skewed target variable for a regression problem. During EDA, the scientist wants to transform the target variable to approximate a normal distribution. Which transformation should the scientist apply first?

Accepted Answer

Box-Cox transformation. Box-Cox transformation (D) is a parametric transformation that identifies the optimal power transformation to make data more normally distributed. For skewed target variables in regression, it is often preferred as a first approach because it can handle various skewness patterns and includes log transformation as a special case (lambda=0). Quantile transformation (A) is non-parametric and can overfit; Min-Max scaling (B) only rescales range, not shape; Log transformation (C) is a specific case that works for positive data but may not be optimal for all skewness. Therefore, Box-Cox is the best first choice.

Answer

Quantile transformation

Answer

Min-Max scaling

Answer

Log transformation

Question 6

During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A (PCA) is wrong because dimensionality reduction does not address class imbalance. Option B (removing outliers) is wrong because it may worsen imbalance and is not a standard technique for imbalance. Option C (cross-validation) is a model evaluation technique, not a solution for imbalance. Option D (feature scaling) does not affect class distribution.

Answer

Apply PCA to reduce dimensionality

Answer

Remove outliers from the majority class

Answer

Use cross-validation to evaluate the model

Answer

Apply feature scaling to all features

Question 7

An engineer runs the AWS CLI command in the exhibit to create a SageMaker endpoint configuration. The endpoint is created successfully, but when invoked, the inference response is slow. The engineer wants to test with a different instance type. Which action should the engineer take?

Accepted Answer

Update the endpoint configuration with the new instance type and then update the endpoint. To change the instance type for an existing SageMaker endpoint, you must first update the endpoint configuration with the new instance type, then update the endpoint to use the updated configuration. Option D describes this correct procedure. Option B is incorrect because the update-endpoint API does not directly accept an instance type parameter; it updates the endpoint to use a new endpoint configuration. Option A creates a new endpoint, which is less efficient. Option C deletes the endpoint unnecessarily.

Answer

Create a new endpoint configuration and use it to create a new endpoint

Answer

Modify the existing endpoint directly using the update-endpoint API with a new instance type parameter

Answer

Delete the endpoint and create a new one with the desired instance type

Question 8

A machine learning team is using SageMaker Pipelines to orchestrate a multi-step workflow. The pipeline fails with a 'ThrottlingException' when submitting a training job. Which TWO actions can reduce the likelihood of throttling?

Accepted Answer

Implement retry logic with exponential backoff in the pipeline. ThrottlingException occurs when the API request rate exceeds service limits. Implementing retry logic with exponential backoff (option B) helps handle transient throttling by automatically retrying requests with increasing delays. Reducing the number of concurrent pipeline steps (option D) decreases the rate of API calls, reducing the likelihood of hitting rate limits. Option A (Model Registry) is unrelated to throttling. Option C (increasing parallel training jobs) would increase concurrent API calls, worsening throttling. Option E (requesting a quota increase) raises the limit but does not reduce the immediate likelihood of throttling; it is a longer-term mitigation, not a direct action to reduce throttling in the current pipeline.

Answer

Use SageMaker Model Registry to version models

Answer

Increase the number of parallel training jobs

Answer

Request a service quota increase for training jobs

Question 9

A machine learning engineer needs to store and version datasets for reproducibility. Which AWS service is designed for this purpose?

Accepted Answer

SageMaker Feature Store. SageMaker Feature Store is designed to store, manage, and version features for machine learning, ensuring reproducibility. Option A (CodeCommit) is for version control of code. Option B (S3) is an object store not specialized for ML features. Option D (Redshift) is a data warehouse. Therefore, Option C is correct.

Answer

AWS CodeCommit

Answer

Amazon S3

Answer

Amazon Redshift

Question 10

A model deployed on a SageMaker endpoint is producing predictions that are consistently biased against a certain demographic. Which step should the team take FIRST to address this issue?

Accepted Answer

Use SageMaker Clarify to analyze bias in the training data and predictions. The first step is to analyze the data and model for bias. SageMaker Clarify can detect bias in training data and predictions, making option C the correct first step. Option A (SageMaker Model Monitor) tracks prediction quality but does not specifically analyze bias. Option B (switching algorithm) is a reactive change without understanding the bias source. Option D (retraining with balanced data) is a potential fix but should come after bias analysis.

Answer

Enable SageMaker Model Monitor to track prediction quality

Answer

Switch to a different algorithm that is less prone to bias

Answer

Retrain the model with balanced data

Question 11

A company wants to track and compare metrics from multiple machine learning experiments. Which Amazon SageMaker feature should be used?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct choice for tracking and comparing metrics from multiple machine learning experiments. SageMaker Model Monitor is used to detect data drift, SageMaker Debugger is used to debug training jobs, and SageMaker Ground Truth is used for data labeling. Thus, only option A is correct.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Question 12

A data scientist needs to run a one-time SQL query on a large dataset in S3 to create a training dataset. The query involves aggregations and joins. Which service is most suitable?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless service that allows running SQL queries directly on data stored in S3, ideal for one-time ad-hoc queries with aggregations and joins. Option A (AWS Glue ETL) is designed for scheduled ETL jobs, not ad-hoc queries. Option C (Amazon EMR with Spark SQL) provides powerful processing but is overkill and requires cluster management. Option D (Amazon RDS) would require moving data into a database, which is inefficient.

Answer

AWS Glue ETL job

Answer

Amazon EMR with Spark SQL

Answer

Amazon RDS with data loaded into it

Question 13

A data scientist examines a dataset with 100 features and suspects that some features are redundant due to high pairwise correlations. Which EDA technique should the scientist use to systematically identify groups of highly correlated features?

Accepted Answer

Generate a correlation matrix and visualize it as a heatmap.. A correlation matrix heatmap allows systematic identification of groups of highly correlated features by visually highlighting high pairwise correlations. Option B is incorrect because histograms show univariate distributions, not relationships between features. Option C is incorrect because scatter plots for each pair would be time-consuming and not systematic for 100 features. Option D is incorrect because box plots show outliers, not correlations.

Answer

Plot histograms for each feature.

Answer

Create scatter plots for each pair of features.

Answer

Use box plots to identify outliers.

Question 14

A data scientist is exploring a dataset with 10 features and observes that the correlation between feature A and feature B is 0.98. Which action should be taken to address multicollinearity before training a linear regression model?

Accepted Answer

Remove one of the two features from the dataset.. Dropping one of the highly correlated features reduces redundancy and mitigates multicollinearity. Option A (PCA) creates orthogonal components, which addresses multicollinearity but reduces interpretability; dropping a feature is more straightforward. Option B (Min-Max scaling) does not address collinearity at all. Option D is wrong because adding polynomial features increases correlation.

Answer

Use Principal Component Analysis (PCA) to combine them.

Answer

Apply Min-Max scaling to both features.

Answer

Add polynomial features to both.

Question 15

A machine learning team is reviewing a dataset for a regression problem. They notice that the target variable has a right-skewed distribution. Which transformation should they consider applying to the target variable to improve model performance?

Accepted Answer

Apply log transformation to the target variable.. Log transformation is commonly applied to right-skewed data to make it more normally distributed, which can improve model performance. Option A (StandardScaler) is for scaling, not skewness. Option B (MinMaxScaler) also doesn't address skewness. Option D (One-hot encoding) is for categorical variables.

Answer

Apply StandardScaler to the target variable.

Answer

Apply MinMaxScaler to the target variable.

Answer

Apply one-hot encoding to the target variable.