MLS-C01 Practice Test 44 — 15 Questions

Question 1

A data scientist is analyzing clickstream data from a website. The data is stored in Amazon S3 as JSON files, each containing nested arrays. The scientist needs to flatten the nested structures and compute user session durations. Which approach is most efficient for this EDA task?

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

Accepted Answer

Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration.. AWS Glue DataBrew provides a visual interface to flatten nested JSON and compute derived metrics like session duration without writing code. Option A (EMR with Apache Spark) is more complex and requires writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option D (QuickSight) is visualization only and cannot flatten or compute session duration.

Answer

Use Amazon EMR with Apache Spark to process the data.

Answer

Use Amazon Athena with JSON SerDe to query the data and compute session duration with SQL.

Answer

Use Amazon QuickSight to visualize the raw data without flattening.

Question 2

A machine learning engineer trains a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. After training, the model achieves 99% accuracy but only 10% recall on the positive class. Which metric should the engineer focus on to evaluate the model's performance on the minority class?

Accepted Answer

F1 score. (F1 score) is the correct metric because it balances precision and recall, providing a single measure that is robust to class imbalance. With only 1% positive class, accuracy (Option B) is misleadingly high due to the majority class. AUC-ROC (Option C) can still be high even if recall is low, as it evaluates ranking rather than absolute performance. Precision (Option D) only considers the proportion of correct positive predictions, ignoring false negatives, which is not suitable when recall is poor. The F1 score captures both aspects, making it the best choice for evaluating minority class performance in this scenario.

Answer

Accuracy

Answer

AUC-ROC

Answer

Precision

Question 3

A data scientist is working with a dataset containing categorical features with high cardinality. The scientist wants to use a tree-based model. Which encoding method should be used?

Accepted Answer

Label encoding. For tree-based models, label encoding (option C) is typically recommended for high-cardinality categorical features because tree models can handle integer encoding without assuming any order—they split on values, not on ordinal relationships. Ordinal encoding (option A) implies an artificial order that may not exist, potentially misleading the model. One-hot encoding (option D) creates too many dimensions. Target encoding (option B) can cause overfitting, especially with high cardinality.

Answer

Ordinal encoding

Answer

Target encoding

Answer

One-hot encoding

Question 4

A data scientist is analyzing a dataset with 10 million rows and 50 columns. The target variable is highly imbalanced (99% negative, 1% positive). Which approach is most appropriate for exploratory data analysis before modeling?

Accepted Answer

Take a stratified sample that preserves the 99:1 ratio.. C is correct because stratified sampling preserves the original class proportion (99:1) in the sample, which is important for exploratory data analysis on imbalanced data without artificially altering the distribution. Option A (remove all negatives) loses all negative data, preventing analysis of majority class patterns. Option B (random sample) may result in insufficient positive examples due to imbalance. Option D (up-sample minority) would change the distribution and could lead to misleading visualizations and statistics during EDA.

Answer

Remove all negative examples and analyze only the positive ones.

Answer

Take a random sample of 100,000 rows from the entire dataset.

Answer

Up-sample the minority class to balance the dataset before analysis.

Question 5

A company is fine-tuning a BERT model on Amazon SageMaker for a text classification task. The training script uses PyTorch and Hugging Face Transformers. The training job completes successfully, but the final model accuracy is low. The dataset has 10,000 labeled samples. What is the most likely cause and solution?

Accepted Answer

The model is overfitting due to small dataset; use a pre-trained checkpoint and fine-tune only top layers. Fine-tuning the entire BERT model on only 10,000 samples leads to overfitting, resulting in low accuracy. The recommended approach is to use a pre-trained checkpoint and fine-tune only the top layers, which leverages transfer learning and reduces the risk of overfitting. Option A (instance type) impacts training speed, not accuracy directly. Option C (learning rate) could be a factor but overfitting is the most likely given the dataset size. Option D (data loader bug) would typically cause errors, not low accuracy without errors.

Answer

The instance type is insufficient; use a larger instance

Answer

The learning rate is too high; reduce it

Answer

The training script has a bug in the data loader

Question 6

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains customer demographic information and purchase history. Which approach should the data scientist take to handle missing values without introducing bias into the dataset?

Accepted Answer

Use multiple imputation to estimate missing values.. Multiple imputation produces multiple estimates of missing values, accounting for the uncertainty in the imputation and reducing bias compared to simpler methods. Option A is wrong because dropping all rows with missing values can lead to loss of data and potential bias if missingness is not completely random. Option B is wrong because mean imputation can underestimate variance and distort relationships between variables. Option C is wrong because replacing missing values with a constant (e.g., 0) introduces arbitrary values that can skew the data distribution.

Answer

Drop all rows with any missing values.

Answer

Impute missing values with the mean of each column.

Answer

Replace missing values with a constant, such as 0.

Question 7

A machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer use?

Accepted Answer

Create a custom inference script and use the SageMaker PyTorch container. SageMaker allows you to provide a custom inference script (entry point) when using the PyTorch container, enabling custom inference logic. Option A is wrong because the built-in container as-is would not incorporate custom logic. Option B is wrong because SageMaker Ground Truth is for labeling, not model deployment. Option C is wrong because SageMaker Processing is for data processing, not inference.

Answer

Use a SageMaker built-in PyTorch container as-is

Answer

Use SageMaker Ground Truth to deploy the model

Answer

Use SageMaker Processing to run inference

Question 8

A data scientist is training a time series forecasting model using Amazon SageMaker's DeepAR algorithm. The dataset contains daily sales data for 10,000 products over 2 years. The scientist splits the data chronologically: training on the first 18 months, validation on the next 3 months, and test on the last 3 months. The model performs well on validation but poorly on test. The data scientist suspects the model is overfitting to the validation period. Which action should the scientist take to improve test performance?

Accepted Answer

Use time series cross-validation with an expanding window. Time series cross-validation with an expanding window evaluates the model on multiple validation periods, ensuring robustness and reducing overfitting to a single validation window. Option B reduces context length and may lose long-term dependencies. Option C may introduce irrelevant features and does not address overfitting to validation. Option D removes validation entirely, which would not help diagnose or reduce overfitting.

Answer

Reduce the context length to 30 days

Answer

Add more exogenous features like holidays and promotions

Answer

Use the entire dataset for training and ignore validation

Question 9

A data scientist is building a regression model to predict energy consumption. The dataset includes features like temperature, humidity, day of week, and holiday flags. The scientist uses a linear regression model and obtains an R-squared of 0.85 on training and 0.40 on test. The scientist suspects the model is not capturing non-linear relationships. Which approach should the scientist use to capture non-linearity?

Accepted Answer

Add polynomial features (e.g., squared terms and interactions). (add polynomial features) captures non-linear relationships by introducing squared terms and interactions, allowing the linear model to fit curvature. Option A (PCA) reduces dimensionality but does not add non-linearity. Option B (L1 regularization using Lasso) reduces overfitting by shrinking coefficients but does not introduce non-linear terms. Option C (removing features with low correlation) may lose information and does not help with non-linearity.

Answer

Apply PCA to the feature set

Answer

Increase L1 regularization using Lasso

Answer

Remove features with low correlation to the target

Question 10

A machine learning team is using SageMaker to train a model with the built-in Linear Learner algorithm. The dataset has 1 million rows and 20 features. The training completes, but the model's mean squared error (MSE) is high. Which parameter adjustment is most likely to reduce MSE?

Accepted Answer

Increase the number of epochs. Increasing the number of epochs allows the model to see the data more times, helping it converge to a lower training error, thus reducing MSE. Option A is incorrect: increasing mini-batch size typically improves computational efficiency but can make convergence slower per epoch, potentially requiring even more epochs to converge; it may not directly reduce MSE. Option B is incorrect: cross-entropy is a loss function for classification problems, not regression; Linear Learner with MSE is appropriate for regression. Option D is incorrect: increasing the learning rate can cause the optimizer to overshoot the minimum or diverge, often increasing rather than decreasing MSE.

Answer

Increase the mini-batch size

Answer

Change the loss function to cross-entropy

Answer

Increase the learning rate

Question 11

Refer to the exhibit. A data scientist ran a SageMaker training job and reviewed the logs. The training completed quickly, but the model performance is very poor. What is the most likely cause?

Accepted Answer

The training dataset is too small for the model complexity.. The training job completed very quickly (about 1 minute), which suggests the dataset is small. A small training dataset, especially relative to the model's complexity, leads to poor performance because the model cannot learn generalizable patterns. With insufficient data, the model may overfit the training samples or fail to converge to a good solution, resulting in poor test performance. Other options are less likely: overfitting (A) would typically show high training accuracy but poor validation accuracy, which is not indicated; data leakage (B) would artificially inflate performance; and a low learning rate (C) would cause slow convergence but not necessarily quick completion.

Answer

The model is overfitting to the training data.

Answer

There is data leakage from the test set into the training set.

Answer

The learning rate is too low, causing slow convergence.

Question 12

A data analyst is performing exploratory data analysis on a dataset and notices that there are outliers in several numerical columns. Which TWO methods can the analyst use to identify outliers?

Accepted Answer

Calculate z-scores and flag any data points with |z| > 3.. Options B and C are correct. Box plots use the IQR to identify outliers as points outside 1.5*IQR from the quartiles (option C). Z-scores identify outliers as points with |z| > 3, assuming a roughly normal distribution (option B). Option A (scatter plot matrix) can help visualize outliers but is not a systematic detection method. Option D (comparing mean and median) provides insight into skewness but does not directly flag outliers. Option E (histogram) shows distribution shape but requires subjective judgment to identify outliers.

Answer

Create a scatter plot matrix to visually inspect.

Answer

Compare the mean and median of each column.

Answer

Plot a histogram and look for gaps.

Question 13

Refer to the exhibit. A data scientist runs the AWS CLI command shown and gets the output. The scientist wants to create an Athena table over all log files in the 'logs/2023/' prefix, including files smaller than 1000 bytes. Which approach achieves this?

Accepted Answer

Create the table using LOCATION 's3://my-bucket/logs/2023/' which includes all files under that prefix.. Creating an Athena table with the LOCATION pointing to 's3://my-bucket/logs/2023/' will include all objects under that prefix, regardless of size. The CLI command's --query parameter only filters the output of the list-objects command, but does not impose any restriction on the data or the bucket. Option B is incorrect because a WHERE clause in Athena can only filter rows after the table is defined; it cannot include or exclude files from being read. Option C is incorrect because S3 does not have a built-in size restriction on a bucket; the CLI command is just a client-side query. Option D is incorrect because the CLI command is independent; the table can be created directly without needing to modify the CLI command.

Answer

Create the table and add a WHERE clause to include small files.

Answer

Ask the S3 team to remove the size restriction on the bucket.

Answer

Modify the CLI command to remove the size filter and re-run it before creating the table.

Question 14

A data analyst is exploring a dataset with a target variable that is highly imbalanced. The minority class represents only 1% of the data. Which technique should the analyst use to better understand the relationships between features and the minority class?

Accepted Answer

Use stratified sampling to create a balanced sample for analysis.. Stratified sampling ensures the minority class is proportionally represented in the sample, allowing meaningful analysis. Option A is wrong because SMOTE generates synthetic data, which is not appropriate for initial exploratory analysis. Option B is wrong because random sampling may miss the minority class entirely. Option C is wrong because scaling features does not address class imbalance.

Answer

Apply SMOTE to the dataset before analysis.

Answer

Use random sampling to reduce the dataset size.

Answer

Scale the features using Min-Max scaling.

Question 15

A machine learning engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 using AWS Glue. The dataset contains a mix of numeric and categorical features. The engineer wants to efficiently compute summary statistics (e.g., mean, median, standard deviation) for the numeric columns. Which AWS service or feature should the engineer use to achieve this with minimal setup?

Accepted Answer

Use AWS Glue DataBrew to profile the dataset.. AWS Glue DataBrew provides a visual interface to profile data and compute summary statistics without writing code. Option A is wrong because launching an Amazon EMR cluster requires setup and management, which is not minimal. Option C is wrong because Amazon Athena requires writing SQL queries and does not automatically compute summary statistics. Option D is wrong because Amazon SageMaker Data Wrangler is a good tool but requires more configuration than DataBrew for simple summary statistics.

Answer

Launch an Amazon EMR cluster and use Spark.

Answer

Use Amazon Athena to run SQL queries on the data.

Answer

Use Amazon SageMaker Data Wrangler.