MLS-C01 Practice Test 42 — 15 Questions

Question 1

A data scientist is exploring a dataset with categorical variables. Which TWO EDA techniques are appropriate for understanding the relationship between a categorical feature and a continuous target? (Choose TWO.)

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

Accepted Answer

Violin plots. Box plots (E) and violin plots (B) are both effective for visualizing the distribution of a continuous variable across different categories. Violin plots combine box plots with kernel density estimation, providing a richer view. Correlation matrix (A) is for numerical variables. Scatter plots (C) require two continuous variables. Bar chart of counts (D) shows frequency distribution of categories, not relationship with a continuous target.

Answer

Correlation matrix

Answer

Scatter plot with categorical variable on x-axis

Answer

Bar chart of category counts

Question 2

Which THREE are common techniques for detecting outliers in a univariate dataset? (Select THREE.)

Accepted Answer

Z-score. Options C, D, and E are correct. Z-score (C) identifies outliers by standard deviations from mean, IQR method (D) uses quartiles to detect outliers beyond 1.5×IQR, and Modified Z-score using MAD (E) is a robust alternative. Cook's distance (A) is a regression diagnostic for influential points, not univariate outlier detection. DBSCAN (B) is a multivariate clustering algorithm.

Answer

Cook's distance

Answer

DBSCAN clustering

Question 3

An ML team is analyzing a time series dataset of daily website traffic. They notice a pattern where traffic spikes every Sunday. Which EDA technique should they use to confirm this seasonality?

Accepted Answer

Plot the time series data with a line plot. A line plot of time series data visually displays trends and repeating patterns, making it the most direct way to confirm weekly seasonality. Option B (autocorrelation) can quantify periodicity but is less intuitive for simple confirmation; it is not a histogram. Option C (scatter plot of traffic vs. day of week) aggregates data by day, losing the sequential order needed to see seasonality over time. Option D (histogram) shows value distribution, not time-dependent patterns.

Answer

Compute autocorrelation at different lags

Answer

Create a scatter plot of traffic vs. day of week

Answer

Plot a histogram of the traffic values

Question 4

A data scientist is analyzing a dataset with 100 features and 10,000 samples. The target variable is highly imbalanced (1% positive class). Which exploratory data analysis step is most critical before model training?

Accepted Answer

Plot the histogram of the target variable. The most critical EDA step for a highly imbalanced target variable is to examine its distribution. Therefore, plotting the histogram of the target variable (D) reveals the imbalance and guides decisions on resampling or evaluation metrics. Option A (PCA) is primarily for dimensionality reduction and not essential for understanding target balance. Option B (correlation matrix) examines feature relationships, not target distribution. Option C (mean imputation) addresses missing values, which is important but not the most critical for handling class imbalance.

Answer

Apply PCA and visualize the first two principal components

Answer

Compute pairwise correlation matrix among all features

Answer

Impute missing values using mean imputation

Question 5

A data scientist is exploring a large dataset (10 TB) stored in Amazon S3. The dataset is in CSV format and has many columns. The scientist wants to quickly compute summary statistics (mean, min, max, count) for each column without moving the data. Which approach is most cost-effective and efficient?

Accepted Answer

Use Amazon Athena with SQL queries. Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in S3 without moving it. It is cost-effective because you pay only for the data scanned per query. For summary statistics like mean, min, max, count, you can use aggregate functions like AVG, MIN, MAX, COUNT in SQL. Option A (SageMaker Data Wrangler) requires importing data into SageMaker, incurring transfer costs and time. Option B (Amazon EMR) requires provisioning a cluster, which adds overhead and cost for a simple summary task. Option C (S3 Select) works on a single object and cannot compute statistics across entire dataset easily; it is more suited for filtering. Option E (AWS Glue DataBrew) is a data preparation tool that may be more expensive and overkill for simple summary statistics.

Answer

Import the data into Amazon SageMaker Data Wrangler

Answer

Launch an Amazon EMR cluster with Spark

Answer

Use S3 Select to compute statistics

Answer

Use AWS Glue DataBrew to profile the data

Question 6

A data scientist is analyzing application logs in JSON format. Based on the exhibit, which EDA insight is most valuable for troubleshooting?

Accepted Answer

There is a recurring NullPointerException error.. The repeated NullPointerException error appears multiple times in the logs, indicating a recurring issue that is most valuable for troubleshooting. Option B is incorrect because the logs show different timestamps, not all the same. Option C is incorrect because connection timeout appears only once, while the NullPointerException is more frequent. Option D is incorrect because the log levels vary, and the majority of logs are not at WARN level.

Answer

All logs occurred at the same timestamp.

Answer

There is a connection timeout issue.

Answer

Most logs are at WARN level.

Question 7

A data scientist is performing exploratory data analysis on a dataset with missing values. They want to understand the distribution of each feature and identify outliers. Which AWS service can be used to create visualizations such as histograms and box plots without writing any code?

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a serverless, machine learning-powered business intelligence service that allows users to create interactive dashboards and visualizations without writing code. Option A is wrong because Amazon EMR is a big data platform, not primarily for visualization. Option B is wrong because AWS Glue is used for ETL, not visualization. Option C is correct. Option D is wrong because Amazon SageMaker Studio requires coding for custom visualizations. Option E is wrong because Amazon Athena is a query service, not a visualization tool.

Answer

Amazon EMR

Answer

AWS Glue

Answer

Amazon SageMaker Studio

Answer

Amazon Athena

Question 8

A machine learning engineer is analyzing a dataset with a large number of features (p >> n). The engineer suspects that many features are irrelevant. Which THREE methods are suitable for feature selection during exploratory data analysis? (Choose THREE.)

Accepted Answer

Remove features with variance below a threshold (e.g., <0.01). In a high-dimensional dataset (p >> n), feature selection is crucial. Option B (Variance Threshold) is suitable because features with low variance (e.g., <0.01) are likely to be constant or near-constant and thus uninformative. Option C (removing features with high pairwise correlation >0.95) helps reduce redundancy and multicollinearity. Option D (mutual information) is a filter method that measures dependency between each feature and the target, allowing selection of the most relevant features. Option A (Lasso regression) is a modeling method that can be used for feature selection but is not typically used during EDA; it is a supervised learning technique. Option E (PCA) is a dimensionality reduction technique that creates new components, not feature selection. Therefore, the correct answers are B, C, D.

Answer

Fit a Lasso regression model and select features with non-zero coefficients

Answer

Apply Principal Component Analysis (PCA) and select top components

Question 9

A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited to 100 Mbps, and the transfer must complete within 5 days. Which TWO options are viable? (Choose TWO.)

Accepted Answer

Use AWS Snowball Edge device to ship the data. AWS Snowball Edge is a physical data transport solution designed for large-scale data transfers over slow or unreliable networks. With 10 TB of data and a 100 Mbps link, the theoretical transfer time is over 9 days, exceeding the 5-day window. Snowball Edge bypasses the network bottleneck entirely by shipping the data via courier. Option E is also correct because AWS Direct Connect provides a dedicated network connection that can offer higher and more consistent bandwidth than the existing internet connection. If provisioned with sufficient capacity (e.g., 1 Gbps), the transfer can complete within the 5-day window. Options A and B are not viable because they cannot overcome the physical bandwidth limitation of 100 Mbps. Option D is not viable because DataSync still relies on the existing internet connection, which is insufficient.

Answer

Use S3 Transfer Acceleration to speed up the transfer

Answer

Use S3 Multipart Upload to upload files in parallel

Answer

Use AWS DataSync over the existing internet connection

Question 10

A data scientist is analyzing a dataset with missing values in a numeric column. The missing rate is 30% and the data is not missing completely at random. Which imputation method should the data scientist avoid to minimize bias?

Accepted Answer

Mean imputation. Mean imputation (Option A) should be avoided when data is not missing completely at random (NMAR) because it can introduce bias by underestimating variance and distorting the relationships between variables. Options B (model-based imputation), C (k-NN imputation), and D (multiple imputation) are more robust for non-random missing data as they account for patterns in the data and produce less biased estimates.

Answer

Model-based imputation using linear regression

Answer

k-Nearest Neighbors imputation

Answer

Multiple imputation using chained equations

Question 11

A company has a time series dataset of daily sales for the past 5 years. They want to forecast sales for the next 30 days. The data shows weekly seasonality and a slight upward trend. Which Amazon SageMaker algorithm is most appropriate for this task?

Accepted Answer

DeepAR. DeepAR is purpose-built for time series forecasting with seasonal patterns and trends. It uses a recurrent neural network (RNN) to model the conditional distribution of future values given past observations, and it natively handles multiple time series, missing data, and known seasonal periods (e.g., weekly). The weekly seasonality and upward trend in the daily sales data are exactly the kind of patterns DeepAR is designed to capture.

Answer

Linear Learner

Answer

XGBoost

Answer

K-Means

Question 12

Refer to the exhibit. A company is using the Kinesis stream 'my-stream' with one shard. The producer is sending 1000 records per second, each 1 KB. The consumer is reading from the stream using the Kinesis Client Library (KCL). The consumer is able to process 500 records per second per shard. What is the most likely cause of the consumer falling behind?

Accepted Answer

The stream uses KMS encryption, which adds latency.. The consumer is processing 500 records per second per shard, which is half the producer's rate of 1000 records per second. While the shard's read throughput limit is 2 MB/s, not 1 MB/s, the consumer's processing speed is the bottleneck. Among the options, KMS encryption is the most plausible cause because decryption latency can significantly slow down the consumer, especially if the consumer application is not optimized for encryption overhead.

Answer

The retention period is set to 24 hours, which is too short.

Answer

The stream has only one shard, which limits the read throughput to 1 MB/s.

Answer

The consumer application is not using enhanced fan-out.

Question 13

A company is deploying a machine learning model on Amazon SageMaker. The model needs to be updated frequently with new versions. The team wants to minimize downtime and test the new model version before routing all traffic to it. Which TWO strategies should be used together?

Accepted Answer

Use Amazon SageMaker canary deployment.. The correct answers are D (canary deployment) and E (blue/green deployment). In Amazon SageMaker, blue/green deployment allows you to deploy a new model version alongside the existing one (blue) and then shift traffic gradually. Canary deployment is a feature of SageMaker that routes a small percentage of traffic to the new version for testing before shifting more. Together, these strategies minimize downtime and allow testing. Option A (rolling update) is not directly supported in SageMaker for endpoints; SageMaker uses deployment variants. Option B (multi-model endpoint) is for hosting multiple models on the same endpoint but does not provide traffic shifting for updates. Option C (A/B testing) in SageMaker is typically achieved using production variants with traffic weights, but the specific feature for gradual traffic shifting is called canary deployment, so option C is incorrect as stated.

Answer

Use a rolling update strategy.

Answer

Use a multi-model endpoint.

Answer

Use Amazon SageMaker A/B testing.

Question 14

A data engineer is performing exploratory data analysis on a dataset with 1 million rows and 50 features. The engineer wants to identify missing values and outliers. Which THREE approaches should the engineer use? (Choose three.)

Accepted Answer

Use a DataFrame.info() method to see non-null counts. Options B, D, and E are correct because they directly help identify missing values and outliers. DataFrame.info() shows non-null counts per column, revealing missing values. The missingno matrix visualizes missing data patterns across rows and columns. DataFrame.describe() provides summary statistics (count, mean, std, min, max, quartiles) that can indicate outliers (e.g., values far outside the interquartile range). Option A (correlation heatmap) is used to assess relationships between features, not to detect missing values or outliers. Option C (box plots for all features simultaneously) is impractical with 50 features because it would be cluttered and hard to interpret; box plots are more useful when applied selectively to a few features or after filtering.

Answer

Create a correlation heatmap of all features

Answer

Plot box plots for all features simultaneously

Question 15

A data engineer is building a data pipeline for a machine learning project using Amazon SageMaker. The raw data is stored in Amazon S3. Which TWO steps are essential to ensure data privacy and security before training? (Choose TWO.)

Accepted Answer

Encrypt the data at rest using S3 server-side encryption. Options C and E are correct because data privacy and security require encryption at rest (e.g., S3 server-side encryption) and secure network access (e.g., using SageMaker in a VPC with VPC endpoints). Option C ensures data is encrypted when stored in S3. Option E prevents data from traversing the public internet and allows fine-grained access control. Options A, B, and D are not essential for privacy/security: A (bucket policy) is a means of access control but not as fundamental as encryption; B (versioning) protects against accidental deletion, not privacy; D (Transfer Acceleration) is a performance feature, not a security measure.

Answer

Create a bucket policy that restricts access to the data scientist's IAM role only

Answer

Enable versioning on the S3 bucket

Answer

Use S3 Transfer Acceleration for faster uploads