MLS-C01 Practice Exam 64 — 20 Questions

Question 1

A data scientist is performing EDA on a dataset with mixed data types (numerical, categorical, text). The dataset is stored in S3. Which TWO AWS services can be used to directly perform statistical summaries and visualizations without writing custom code?

Accepted Answer

Amazon SageMaker Data Wrangler. Options A and D are correct. SageMaker Data Wrangler provides a visual interface for data preparation and analysis with built-in transforms and visualizations. QuickSight is a BI service that can connect to S3 data and create dashboards with statistical summaries. Option B is wrong because Athena is primarily SQL query engine, not visualization. Option C is wrong because Glue DataBrew is for data preparation but requires some configuration. Option E is wrong because SageMaker Studio is an IDE, not a direct analysis service.

Answer

Amazon SageMaker Studio

Answer

AWS Glue DataBrew

Answer

Amazon Athena

Question 2

A data scientist is performing exploratory data analysis on a time-series dataset of website traffic. The dataset contains hourly page views for the past two years. The scientist wants to analyze seasonality and trends. Which THREE techniques are appropriate for this analysis? (Choose THREE.)

Accepted Answer

Moving average smoothing. Decomposition separates time series into trend, seasonal, and residual components. Autocorrelation plot (ACF) helps identify seasonality. Moving average smooths to reveal trends. Linear regression is not typical for seasonal decomposition. Box plot by month can show seasonal patterns but is less common for trend.

Answer

Box plot by month

Answer

Linear regression on time index

Question 3

A data scientist is training a model using Amazon SageMaker and wants to reduce the training time. The training job uses a single GPU instance. Which THREE actions can reduce training time?

Accepted Answer

Use distributed training across multiple GPU instances.. Options A, B, and D are correct. Using multiple GPUs (distributed training), using Pipe input mode, and using a larger instance type with more compute power can reduce training time. Option C is wrong because using more training data increases training time. Option E is wrong because reducing batch size can actually increase training time due to more iterations.

Answer

Increase the amount of training data.

Answer

Reduce the batch size.

Question 4

A data scientist is analyzing a dataset and finds that two features have a Pearson correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose two.)

Accepted Answer

Combine the two features into a single feature using PCA or averaging. Options B and C are correct. High correlation can lead to multicollinearity, so removing one feature (B) or combining them (C) are valid approaches. Option A is wrong because increasing regularization is a remedy for multicollinearity but does not directly address the correlation. Option D is wrong because scaling does not affect correlation. Option E is wrong because adding interaction terms can increase multicollinearity.

Answer

Add interaction terms between the features

Answer

Increase regularization strength in the model

Answer

Apply standard scaling to both features

Question 5

A data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?

Accepted Answer

Use pandas .isnull().sum() in a SageMaker notebook. Using pandas .isnull().sum() in a SageMaker notebook is a standard approach to count missing values per column. Option A is wrong because S3 Select is for filtering S3 objects, not for data analysis. Option B is wrong because QuickSight is for visualization but not for programmatic missing value analysis. Option D is wrong because Athena requires SQL and is less direct for EDA. Option E is wrong because Glue Crawler discovers schema, not missing values.

Answer

Use Amazon S3 Select to query missing values

Answer

Use Amazon Athena to run a SELECT COUNT(*) query

Answer

Use Amazon QuickSight to create a missing value dashboard

Answer

Use AWS Glue Crawler to detect missing values

Question 6

A machine learning engineer is performing exploratory data analysis on a large dataset stored in S3 using Amazon Athena. The dataset contains a timestamp column 'event_time' of type string. The engineer wants to analyze daily trends. Which approach is the most cost-effective and efficient?

Accepted Answer

Partition the table by date derived from the event_time string and query using partition filtering.. Option D is correct because converting the string to a date type in the query allows Athena to use partition pruning if the table is partitioned by date, reducing scanned data. Option A is wrong because CAST in SELECT still scans all data. Option B is wrong because creating a view does not reduce data scanned. Option C is wrong because converting to Parquet is beneficial but not the most direct for the given task.

Answer

Create a view that casts the column to timestamp and query the view.

Answer

Use the CAST function in the SELECT statement to convert the string to timestamp.

Answer

Convert the data to Parquet format with a timestamp column and re-query.

Question 7

A team is using Amazon SageMaker to train a linear regression model on a dataset with 10 features. After training, they notice the model has high bias. Which action is MOST likely to reduce bias?

Accepted Answer

Add polynomial features to capture non-linear relationships. Option D is correct because high bias indicates underfitting, which can be reduced by adding more features or increasing model complexity. Option A reduces risk of overfitting, not bias. Option B increases regularization, which increases bias. Option C reduces data, potentially increasing bias.

Answer

Increase the regularization parameter lambda

Answer

Add L2 regularization

Answer

Use a smaller training dataset

Question 8

A data scientist needs to deploy a trained model to Amazon SageMaker for real-time inference. The model is stored as a .tar.gz file in Amazon S3. Which AWS service is used to create a SageMaker endpoint?

Accepted Answer

SageMaker Model and Endpoint Configuration. Option B is correct because a SageMaker endpoint requires a model and an endpoint configuration. Options A, C, D are not required for creating the endpoint.

Answer

AWS Lambda

Answer

AWS CloudFormation

Answer

Amazon ECS

Question 9

A team needs to automatically retrain a model every week using new data. Which SageMaker feature is designed to schedule and automate this workflow?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines allows building end-to-end ML workflows with scheduling. Option A is correct. Option B is for model monitoring. Option C is for feature engineering. Option D is for automatic model tuning.

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Model Monitor

Answer

SageMaker Data Wrangler

Question 10

A company is using Amazon Forecast for demand forecasting. The data includes time series data for multiple items. The company wants to ensure that the forecast is updated daily as new data arrives. Which approach should be used to automate this process?

Accepted Answer

Use AWS Lambda to invoke the Forecast CreateDatasetImportJob and CreatePredictorBacktestExportJob APIs on a daily schedule triggered by Amazon CloudWatch Events.. Option A is correct because Amazon Forecast Predictor can be updated with new data by using the CreateDatasetImportJob API, and then retraining the predictor. Option B is wrong because Forecast does not support real-time streaming. Option C is wrong because Forecast does not update automatically; manual retraining is required. Option D is wrong because there is no built-in scheduler; custom automation is needed.

Answer

Use Amazon Kinesis Data Streams to stream new data directly into Forecast.

Answer

Use Amazon SageMaker to retrain the model daily and replace the forecast endpoint.

Answer

Enable the 'AutoPredictor' feature in Forecast to automatically update the predictor when new data arrives.

Question 11

A company uses SageMaker to host a real-time inference endpoint. The endpoint is receiving a large number of requests, but the latency is higher than expected. The data scientist observes that the CPU utilization is low but memory utilization is high. Which action should be taken to reduce latency?

Accepted Answer

Switch to an instance type with more memory or optimize the model to reduce memory footprint.. Option A is correct because high memory utilization suggests the model is memory-bound; increasing instance memory or using a model with lower memory footprint can reduce latency. Option B is wrong because CPU utilization is low, so more CPU cores won't help. Option C is wrong because increasing instance count can help throughput but not necessarily latency per request; also it may increase cost. Option D is wrong because the issue is memory, not network.

Answer

Enable VPC traffic mirroring to diagnose network issues.

Answer

Use an instance type with more vCPUs.

Answer

Increase the number of instances in the endpoint.

Question 12

A data scientist is exploring a dataset with a binary target variable. Which TWO metrics are appropriate for evaluating the balance of the target classes? (Choose two.)

Accepted Answer

Count plot of the target variable. Options A and D are correct. Count plot and value_counts show class frequencies. Option B is wrong because correlation matrix shows relationships between features. Option C is wrong because scatter plot shows relationship between two numeric variables. Option E is wrong because histogram shows distribution of a continuous variable.

Answer

Histogram of a feature

Answer

Scatter plot of two features colored by target

Answer

Correlation matrix of all features

Question 13

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. Using Amazon Athena with SQL queries allows serverless querying of data in S3, and is cost-effective (pay per query). It can compute summary statistics directly on the data without moving it. Option A is wrong because SageMaker Data Wrangler requires importing data into SageMaker, which may incur transfer costs and time. Option B is wrong because Glue DataBrew also processes data but may be more expensive for large datasets. Option D is wrong because S3 Select works on single objects and is limited. Option E is wrong because launching an EMR cluster adds overhead and cost for a simple task.

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 14

An organization stores streaming data in Amazon Kinesis Data Streams. A data analyst wants to perform real-time exploratory data analysis on the incoming data to detect anomalies. Which AWS service should the analyst use to run SQL queries on the streaming data?

Accepted Answer

Amazon Kinesis Data Analytics. Option B is correct because Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time. Option A is wrong because Athena is for batch queries on S3. Option C is wrong because Glue is for ETL, not real-time SQL. Option D is wrong because SageMaker is for ML model training, not streaming SQL.

Answer

Amazon SageMaker

Answer

AWS Glue

Answer

Amazon Athena

Question 15

A data scientist is analyzing a dataset with high cardinality categorical features (e.g., user IDs with millions of unique values). They want to visualize the relationship between these categorical features and a continuous target variable. Which approach is most effective for EDA?

Accepted Answer

Group rare categories into an 'Other' category and use box plots. For high cardinality categorical features, grouping rare categories into an 'Other' category reduces cardinality and allows meaningful visualizations like box plots. Option A is wrong because removing the feature loses information. Option B is wrong because one-hot encoding creates too many columns and is not suitable for visualization. Option D is wrong because visualizing millions of categories is not feasible. Option E is wrong because feature hashing is for modeling, not EDA visualization.

Answer

Apply one-hot encoding and use scatter plots

Answer

Use a bar chart with all categories on x-axis

Answer

Remove the categorical features from analysis

Answer

Apply feature hashing and visualize the hashed values

Question 16

A data scientist is analyzing a dataset with several categorical features and a binary target. The scientist wants to check for association between each categorical feature and the target. Which THREE statistical tests are appropriate?

Accepted Answer

Chi-square test of independence. Options A, B, and D are correct. Chi-square test of independence is for categorical-categorical association. Cramér's V is a measure of association based on chi-square. Mutual information is a non-parametric measure that can capture non-linear dependencies. Option C is wrong because ANOVA is for categorical vs continuous. Option E is wrong because Pearson correlation is for continuous variables.

Answer

ANOVA

Answer

Pearson correlation coefficient

Question 17

A data engineer is exploring a large dataset in Amazon Athena. The dataset is partitioned by date and stored in Parquet format. The engineer wants to check the number of distinct values in a column for a specific date range. Which THREE practices reduce query cost and improve performance?

Accepted Answer

Use the COUNT(DISTINCT column) function.. Options A, C, and D are correct. Using partition filtering limits data scanned. Using COUNT(DISTINCT) is efficient but still scans; however, the question asks for reducing cost, so partition filtering is key. Option B is wrong because SELECT * scans all columns. Option E is wrong because ORDER BY without LIMIT requires full scan and sort.

Answer

Use ORDER BY to sort the results.

Answer

Use SELECT * to retrieve all columns.

Question 18

A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is failing with a 'ResourceLimitExceeded' error. The engineer checks the account limits and sees that the current limit for the instance type is 2, and they are already using 2 instances for other jobs. Which approach would resolve the issue MOST cost-effectively?

Accepted Answer

Use a different instance type that is available and has sufficient capacity. Option B is correct because using a different instance type within the same family often has separate limits. Option A increases cost. Option C may not resolve if the limit is account-wide. Option D changes the request, not the limit.

Answer

Request a service limit increase for the current instance type

Answer

Use a managed spot training instead of on-demand

Answer

Stop the other training jobs to free up resources

Question 19

Which THREE techniques are commonly used in exploratory data analysis to understand the relationships between features and the target variable? (Select THREE.)

Accepted Answer

Use box plots to compare feature distributions across target classes.. Options A, C, and E are correct. A: Correlation matrix quantifies linear relationships. C: Pair plots allow visual inspection of multiple relationships. E: Box plots by target show distribution differences. B is wrong because PCA is for dimensionality reduction, not EDA of relationships. D is wrong because clustering is unsupervised and not directly for feature-target relationships.

Answer

Perform K-means clustering on the features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensions.

Question 20

A company is deploying a machine learning model using Amazon SageMaker. To reduce costs, they want to use SageMaker Managed Spot Training. Which THREE conditions must be met for the training job to use spot instances? (Choose THREE.)

Accepted Answer

The training job must be able to handle interruptions gracefully. Options A, C, and D are correct. Spot training requires checkpointing (A), the instance type must support spot (C), and the training job must be stoppable (D). Option B is not required — spot can be used in a VPC. Option E is not required; spot is only for training, not inference.

Answer

The model must be deployed to a serverless endpoint

Answer

The training job must be configured to run in a VPC