MLS-C01 Practice Test 30 — 15 Questions

Question 1

In exploratory data analysis, a data scientist notices that the distribution of a continuous variable is bimodal. The scientist suspects that the two modes correspond to two different groups in the data. Which visualization is MOST appropriate to confirm this suspicion?

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

Accepted Answer

Histogram with overlaid densities by group. The most appropriate because a histogram with overlaid densities, colored by group, directly shows the distribution of each group and can reveal whether the two modes correspond to different groups. Option A (box plot) displays summary statistics but not the shape or modality. Option B (bar chart) is for categorical data, not continuous. Option D (scatter plot) is for two continuous variables, not for examining a single distribution.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 2

Which THREE techniques are commonly used to detect outliers in a dataset? (Select THREE.)

Accepted Answer

Interquartile range (IQR). Options A, D, and E are correct. Z-score and IQR are standard statistical methods for identifying outliers. Isolation Forest is a machine learning algorithm specifically designed for anomaly detection. Option B (k-means clustering) is incorrect because it is a clustering algorithm, not typically used for outlier detection. Option C (PCA) is incorrect because principal component analysis is used for dimensionality reduction, though it can be used in some outlier detection contexts, it is not one of the three most common techniques.

Answer

k-means clustering

Answer

Principal component analysis (PCA)

Question 3

A data scientist is performing EDA on a dataset with 10 million rows. The dataset has a column 'income' with outliers. The data scientist wants to detect and handle outliers. Which THREE approaches are appropriate?

Accepted Answer

Calculate z-scores and flag values beyond 3 standard deviations. The correct approaches for detecting outliers in a dataset with 10 million rows are calculating z-scores (A), using IQR (E), and visualization with box plots (D). Z-scores flag values beyond 3 standard deviations, IQR identifies outliers as points below Q1-1.5*IQR or above Q3+1.5*IQR, and box plots provide a visual summary of the distribution. Min-max scaling (B) only transforms the data range and does not detect outliers. One-hot encoding (C) is for categorical variables, not outlier detection. Thus options A, D, and E are correct.

Answer

Apply min-max scaling to the column

Answer

Convert the column to one-hot encoding

Question 4

Which TWO of the following are common techniques for detecting outliers in a dataset?

Accepted Answer

Z-score. Z-score identifies outliers based on standard deviations from the mean. IQR method uses quartile ranges to flag points outside 1.5*IQR. Standard scaling, PCA, and K-means are not primarily outlier detection methods.

Answer

Principal Component Analysis (PCA)

Answer

K-means clustering

Answer

Standard scaling

Question 5

A data scientist is performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)

Accepted Answer

Create an indicator column to flag whether the value was missing, then impute with a placeholder. Options C and E are correct. Creating an indicator column to flag missingness and then imputing with a placeholder (e.g., mean/median) is a common technique to preserve information about missing patterns. Imputing numerical columns with median and categorical with mode is robust to outliers and preserves distribution. Option A (forward fill) is typically used for time series data, not general tabular EDA. Option B (listwise deletion) can reduce sample size and introduce bias if data is not missing completely at random. Option D (mean imputation) is sensitive to outliers and can distort variance.

Answer

Use forward fill to propagate the last observed value

Answer

Remove all rows with any missing value (listwise deletion)

Answer

Impute missing values with the mean of each column

Question 6

A data scientist is exploring a dataset with skewed numerical features. Which THREE transformations can help make the features more normally distributed?

Accepted Answer

Yeo-Johnson transformation. Correct options: C, D, E. Yeo-Johnson transformation (C), Box-Cox transformation (D), and log transformation (E) are all effective for making skewed numerical features more normally distributed. Option A, min-max scaling, only rescales the feature to a fixed range and does not change the distribution shape. Option B, standardization (Z-score), centers and scales the data but does not alter skewness.

Answer

Min-max scaling

Answer

Standardization (Z-score)

Question 7

During exploratory data analysis, a machine learning engineer finds that a dataset has a significant number of missing values in a categorical feature with 10 levels. Which approach should they take to handle these missing values before modeling?

Accepted Answer

Create a new category labeled 'Missing' for missing values.. Creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because mean imputation is for numerical features, not categorical. Option C is incorrect because dropping all rows with missing values may discard valuable data and reduce sample size. Option D is incorrect because mode imputation may introduce bias if missingness is not random.

Answer

Impute missing values with the mean of the feature.

Answer

Drop all rows with missing values.

Answer

Impute missing values with the mode of the feature.

Question 8

A company uses SageMaker Pipelines to automate model retraining. The pipeline fails intermittently at the Preprocess step with a 'ResourceLimitExceeded' error. The team uses a ml.m5.xlarge instance. What is the most likely cause?

Accepted Answer

The account has reached the limit for concurrent ml.m5.xlarge instances. The error indicates reaching a service quota. SageMaker has a default limit of concurrent training jobs per account. Option A is correct. Option B would cause different errors. Option C is unrelated. Option D would cause a SageMaker service error, not a resource limit.

Answer

The preprocessing script has a memory leak

Answer

The S3 bucket has insufficient permissions

Answer

The pipeline execution role is missing the PassRole permission

Question 9

Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high?

Accepted Answer

Recall. When false negatives are costly, we want to minimize them, so recall (true positive rate) is important. Precision is also important to avoid too many false positives, but F1 score balances both. Recall directly measures false negatives, and F1 combines precision and recall. AUC-ROC is a general measure, and accuracy can be misleading. Therefore, the two appropriate metrics are Recall (option C) and F1 score (option D).

Answer

Accuracy

Answer

AUC-ROC

Answer

Precision

Question 10

A data scientist is training a neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?

Accepted Answer

Implement early stopping. Increasing validation loss while training loss decreases indicates overfitting. Early stopping (Option A) halts training when validation loss stops improving, directly preventing overfitting. Option B (adding more layers) increases model capacity and typically worsens overfitting. Option C (reducing learning rate) might slow training but does not directly stop overfitting. Option D (increasing epochs) would continue training and likely worsen overfitting.

Answer

Add more layers to the network

Answer

Reduce the learning rate

Answer

Increase the number of epochs

Question 11

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. The scientist notices that GPU utilization is low and data loading is the bottleneck. Which action should the scientist take to improve training performance?

Accepted Answer

Use Pipe mode for the training data channel. Low GPU utilization with a data loading bottleneck indicates that the CPU cannot feed data to the GPU quickly enough. SageMaker's Pipe mode streams training data directly from S3 without first downloading it to the instance's local storage, reducing I/O overhead and improving data throughput. Option A: Increasing the number of training instances does not address the per-instance data loading bottleneck; each GPU would still be underutilized. Option C: Changing to a CPU instance would be slower because CPUs are less efficient for deep learning training. Option D: Reducing the batch size would decrease GPU utilization further, exacerbating the underutilization problem.

Answer

Increase the number of training instances

Answer

Change the instance type to a CPU instance

Answer

Reduce the batch size

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?

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

Answer

Amazon SageMaker K-Means

Answer

Amazon SageMaker Factorization Machines

Question 13

A company uses Amazon SageMaker to train a linear regression model. The training data includes a feature 'age' with values ranging from 0 to 100. The model's loss is not converging. What is the MOST likely cause?

Accepted Answer

The features are not normalized.. Normalizing features (e.g., scaling 'age' to a similar range as other features) is critical for linear regression convergence. With 'age' ranging 0-100, if other features are in smaller ranges, the optimizer may struggle. Option B (outliers) can cause issues but is less likely than lack of normalization. Option C (instance type too small) typically affects training time, not convergence. Option D (learning rate too high) can cause divergence, but normalization is a more common first step.

Answer

There are outliers in the target variable.

Answer

The instance type is too small.

Answer

The learning rate is too high.

Question 14

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. Regularization (L1/L2), reducing n-gram range to unigrams, and feature selection (removing rare words) all reduce model complexity and help prevent overfitting. Option A (increasing max_features) increases complexity and can worsen overfitting. Option E (TF-IDF) is a weighting scheme, not a regularization technique.

Answer

Increase max_features to include more words

Answer

Use TF-IDF instead of raw counts

Question 15

A team is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking a long time, and the team suspects that data loading is the bottleneck. The dataset consists of many small files (average size 10KB). Which change would most effectively reduce the I/O bottleneck?

Accepted Answer

Combine the small files into larger files (e.g., TFRecord format). Combining small files into larger ones (e.g., TFRecord or Parquet) reduces the number of S3 GET requests and improves throughput. Using Pipe mode reads data sequentially, which is less efficient for random access. Increasing instance count or using P3 instances addresses compute, not I/O. Amazon EFS is not recommended for training jobs due to higher latency.

Answer

Use SageMaker Pipe mode instead of File mode

Answer

Increase the number of training instances

Answer

Use a P3 instance type for better GPU performance