MLS-C01 Practice Exam 60 — 20 Questions

Question 1

A data scientist is analyzing a dataset with a time series component. They suspect there is a weekly seasonality. Which technique should they use to confirm this?

Accepted Answer

Compute autocorrelation function (ACF). The autocorrelation function (ACF) measures the correlation between a time series and its lagged values. A significant spike at lag 7 confirms weekly seasonality. Option A (line chart) is subjective and not a definitive test. Option C (Fourier transform) identifies frequency components but is more complex and less direct for confirming seasonality. Option D (moving average) smooths the series and may obscure seasonality.

Answer

Plot the time series line chart

Answer

Perform Fourier transform

Answer

Compute a 7-day moving average

Question 2

Refer to the exhibit. A data scientist runs the AWS CLI command shown to explore the contents of an S3 bucket. The command returns an empty array. However, the data scientist knows there are objects larger than 1000 bytes in the bucket. What is the most likely reason for the empty result?

Accepted Answer

The AWS CLI is not configured with the correct region for the bucket. The command syntax is correct: `--query 'Contents[?Size > `1000`]'` uses backticks for the numeric literal as per JMESPath. The empty result indicates the CLI could not find matching objects. Since the data scientist knows objects larger than 1000 bytes exist, the most likely cause is that the CLI is configured with a default region different from the bucket's region. Running the command without an explicit `--region` parameter causes it to query the bucket in the wrong region, returning no results. Option A is incorrect because the backticks are valid. Option B is incorrect because `list-objects-v2` supports `--query`. Option C is incorrect because `--query` is supported. Option D is correct because a region mismatch would cause the CLI to look for the bucket in the wrong location, resulting in an empty array even though the bucket contains objects.

Answer

The query syntax is incorrect; backticks should not be used

Answer

The command should use list-objects instead of list-objects-v2

Answer

The --query parameter is not supported by list-objects-v2

Question 3

During EDA, a data scientist discovers that a numerical feature 'income' has a skewness of 3.5. Which transformation should the scientist apply to make the distribution more symmetric?

Accepted Answer

Log transformation. A log transformation is commonly applied to right-skewed positive data to reduce skewness and make the distribution more symmetric. Option A is wrong because standardization (Z-score) centers and scales the data but does not change the shape of the distribution. Option B is wrong because a square transformation would increase skewness for right-skewed data. Option D is wrong because min-max scaling rescales the data to a fixed range but does not alter the distribution's skewness.

Answer

Standardization (Z-score)

Answer

Square transformation

Answer

Min-Max scaling

Question 4

A machine learning engineer notices that the target variable in a regression dataset has a long-tailed distribution. Which visualization technique is most appropriate to assess the distribution before applying a log transformation?

Accepted Answer

Histogram with density curve. (Histogram with density curve) is the most appropriate visualization for assessing a long-tailed distribution because it clearly shows the shape, spread, and tail behavior of the target variable. A histogram with an overlaid density curve helps identify skewness and the need for a log transformation. Option A (Bar chart) is for categorical data, not continuous distributions. Option C (Box plot) provides quartiles and outliers but does not fully reveal the distribution shape, especially the length of tails. Option D (Scatter plot) visualizes relationships between two variables, not a univariate distribution.

Answer

Bar chart

Answer

Box plot

Answer

Scatter plot

Question 5

Which THREE techniques are commonly used to detect multicollinearity in a dataset during exploratory data analysis?

Accepted Answer

Eigenvalue analysis from PCA. Options B, C, and D are correct. B: Eigenvalue analysis from PCA can detect multicollinearity; if some eigenvalues are near zero, it indicates high multicollinearity. C: Correlation matrix shows pairwise correlations between features; high correlation coefficients (e.g., >0.8) indicate collinearity. D: Variance Inflation Factor (VIF) quantifies how much a feature's variance is inflated due to multicollinearity; VIF >10 is often considered problematic. Option A is incorrect because a heatmap of missing values visualizes missing data, not relationships between features. Option E is incorrect because a scatter matrix shows pairwise scatter plots, which can reveal linear relationships but is not a quantitative measure for multicollinearity.

Answer

Heatmap of missing values

Answer

Scatter matrix of all features

Question 6

A data scientist is working on a project to predict customer churn for a telecom company. The dataset includes 50,000 records with 20 features, including customer demographics, account information, and service usage. The data scientist uses Amazon SageMaker Studio and loads the data into a pandas DataFrame. During EDA, the data scientist notices that the target variable 'churn' has only 10% positive cases. Additionally, several features have missing values: 'income' has 5% missing, 'age' has 2% missing, and 'total_charges' has 1% missing. The data scientist also observes that 'income' is highly skewed with a long right tail, and 'age' is moderately skewed. The data scientist wants to handle missing values and prepare the data for modeling. Which course of action is most appropriate?

Accepted Answer

Impute 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.. Median imputation is robust to skewness (particularly for income and age), and SMOTE is applied after splitting to avoid data leakage and handle class imbalance. Option B is wrong because removing rows with missing values would discard roughly 8% of the data (5%+2%+1% with potential overlap), which is a significant loss of information; additionally, random oversampling may lead to overfitting. Option C is wrong because mode imputation is appropriate for categorical data, not for continuous features like income, age, and total_charges. Option D is wrong because mean imputation is sensitive to outliers and skewness, especially for income with a long right tail; also, stratified sampling only ensures proportional representation in train/test splits, it does not generate synthetic samples to address imbalance.

Answer

Remove all rows with any missing values, and use random oversampling to handle class imbalance.

Answer

Impute 'income' with mode, 'age' with mode, 'total_charges' with mode, and use SMOTE after splitting.

Answer

Impute all missing values with the mean of each column, and use stratified sampling to handle class imbalance.

Question 7

During EDA, a data scientist finds that a feature has a skewness value of 2.5. What does this indicate about the data distribution?

Accepted Answer

The distribution is right-skewed. A skewness value of 2.5 is positive and greater than 1, indicating a highly right-skewed (positively skewed) distribution, where the tail extends to the right. Option A correctly identifies this. Option B is wrong because symmetric distributions have skewness near 0. Option C is wrong because left-skewed distributions have negative skewness. Option D is wrong because skewness measures asymmetry, not necessarily the presence of outliers.

Answer

The distribution is symmetric

Answer

The distribution is left-skewed

Answer

The distribution has no outliers

Question 8

A data scientist is performing exploratory data analysis on a dataset with mixed data types: numerical, categorical, and text. They want to use Amazon SageMaker Data Wrangler to create a quick visualization dashboard. Which set of transformations should they apply in Data Wrangler to handle all data types appropriately?

Accepted Answer

Use the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.. Amazon SageMaker Data Wrangler provides built-in analysis types that are appropriate for EDA with mixed data types: summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These allow quick visualization without complex transformations. Option B is incorrect because one-hot encoding and scatter matrix are not suitable for mixed types, and Data Wrangler does not offer a scatter matrix as a built-in analysis. Option C is incorrect because TF-IDF vectorization and k-means clustering are feature engineering and modeling steps, not EDA. Option D is incorrect because PCA is for dimensionality reduction and not a standard EDA visualization technique.

Answer

Convert all features to numerical using one-hot encoding and then create a scatter matrix.

Answer

Apply TF-IDF vectorization to text and then run k-means clustering.

Answer

Use PCA to reduce dimensionality and then visualize the first two components.

Question 9

A data scientist needs to analyze a dataset stored in Amazon S3 as CSV files. The dataset contains 100 columns, and the data scientist wants to quickly understand the distribution of each column, including missing values, data types, and basic statistics. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew (Option A) is correct because it provides visual data profiling and preparation without writing code, allowing users to quickly understand distributions, missing values, data types, and basic statistics. Option B (Amazon SageMaker Data Wrangler) is designed for data preparation and feature engineering within the SageMaker ecosystem, but it is more focused on transforming data for machine learning rather than initial exploratory analysis. Option C (Amazon QuickSight) is a business intelligence tool for creating visualizations and dashboards, not for profiling raw datasets. Option D (Amazon Athena) is an interactive query service that can analyze data in S3 using SQL, but it does not offer built-in data profiling capabilities for quick exploration.

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight

Answer

Amazon Athena

Question 10

A team is using SageMaker to train a model. They want to track hyperparameters, metrics, and model artifacts. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct choice because it is purpose-built for tracking hyperparameters, metrics, and model artifacts across training runs. It automatically captures input parameters, output metrics, and artifact locations (e.g., S3 paths) for each trial, enabling comparison and lineage tracking without manual logging.

Answer

SageMaker Pipelines

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Question 11

A company has a large dataset of customer transactions stored in Amazon Redshift. A data scientist wants to perform EDA using Python libraries like pandas and matplotlib. The dataset is too large to fit into memory on a single EC2 instance. What is the most efficient approach?

Accepted Answer

Use Amazon Athena Federated Query to run SQL queries against Redshift and retrieve aggregated results. Amazon Athena Federated Query can query data in Amazon Redshift directly, allowing the data scientist to run SQL queries that aggregate the data before returning results. This avoids moving the entire dataset and reduces memory usage. Option A is wrong because even with a large EBS volume, the data must still be loaded into memory (pandas DataFrame) on the notebook instance, which may not fit. Option C is wrong because using SQLAlchemy to read the entire table into a pandas DataFrame would require loading all data into memory, causing an out-of-memory error. Option D is wrong because exporting to S3 and then reading with pandas still requires loading the entire dataset into memory, which is inefficient for large datasets.

Answer

Launch an Amazon SageMaker notebook instance with an attached EBS volume large enough to hold the data

Answer

Use a SQLAlchemy connection to read the entire table into a pandas DataFrame and sample it

Answer

Export the Redshift table to Amazon S3 in Parquet format, then use pandas to read the Parquet files

Question 12

A machine learning team is deploying a model that performs real-time inference on streaming data from Amazon Kinesis Data Streams. The model requires sub-100ms latency. Which deployment option should the team choose?

Accepted Answer

Deploy the model on an Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints provide low-latency inference suitable for sub-100ms requirements. SageMaker batch transform (Option A) is for offline predictions. SageMaker asynchronous inference (Option B) is for near-real-time with longer latencies. AWS Lambda (Option D) may not meet sub-100ms consistently due to cold starts and limited compute. Therefore, Option C is correct.

Answer

Use Amazon SageMaker batch transform

Answer

Use Amazon SageMaker asynchronous inference

Answer

Deploy a custom inference container on AWS Lambda

Question 13

A data scientist is tuning a gradient boosting model using SageMaker automatic model tuning. The hyperparameter 'num_round' ranges from 50 to 500. The tuning job uses 'ObjectiveMetric' = 'validation:auc'. After 50 training jobs, the best objective value is 0.95. The data scientist suspects overfitting. What should the data scientist do?

Accepted Answer

Add an early stopping round and increase the range for regularization hyperparameters like 'gamma' and 'lambda'.. Adding an early stopping round prevents training after validation performance stops improving, and increasing the range of regularization hyperparameters like 'gamma' (minimum loss reduction) and 'lambda' (L2 regularization) helps penalize overly complex models, reducing overfitting. Option A (increasing 'max_depth') would allow deeper trees that can memorize noise, worsening overfitting. Option C (increasing 'num_round' to 1000) with no regularization and no early stopping would likely lead to further overfitting. Option D (decreasing 'num_round' to 10-100) might underfit, but it does not address the root cause of overfitting and could reduce performance.

Answer

Increase 'max_depth' to capture more complex patterns.

Answer

Increase 'num_round' to 1000 and keep other hyperparameters unchanged.

Answer

Decrease the range of 'num_round' to 10-100.

Question 14

A data scientist is using Amazon Athena to query a CSV file stored in S3. The query fails with the error: 'HIVE_CANNOT_OPEN_SPLIT: Number of fields in line 1502 does not match number of fields in the first line.' What is the most likely cause?

Accepted Answer

The CSV file has inconsistent number of columns in some rows.. The error indicates that a row has more fields than the header, which is exactly what happens when the CSV file has inconsistent number of columns in some rows. Option A is incorrect because the error does not mention delimiter; a different delimiter would cause all rows to have wrong number of fields, not just some. Option B is incorrect because missing header would cause Athena to treat the first row as data, not cause mismatched field counts later. Option C is incorrect because Athena can handle large files; the error is about schema mismatch, not file size.

Answer

The CSV file uses a different delimiter than comma.

Answer

The CSV file is missing a header row.

Answer

The CSV file is too large for Athena to process.

Question 15

A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?

Accepted Answer

Add polynomial features to capture non-linear relationships.. High bias indicates underfitting, meaning the model is too simple to capture underlying patterns. Adding polynomial features increases model complexity, allowing it to better fit the training data and reduce bias. Option B is incorrect because increasing L2 regularization strength penalizes large coefficients, which increases bias and makes underfitting worse. Option C is incorrect because using a simpler model (e.g., linear regression without interaction terms) would further increase bias. Option D is incorrect because reducing training data does not address bias; it can increase variance, but bias remains high.

Answer

Increase L2 regularization strength.

Answer

Use a simpler model, such as linear regression without interaction terms.

Answer

Reduce the amount of training data.

Question 16

A machine learning engineer is deploying a model using Amazon SageMaker and wants to automatically scale the endpoint based on the number of incoming requests. Which scaling policy should be used?

Accepted Answer

Target tracking scaling. Amazon SageMaker endpoints support Application Auto Scaling. A target tracking scaling policy (Option C) is the recommended approach when you want to automatically scale based on a metric like InvocationsPerInstance. It adjusts capacity to maintain the target value of the metric. Step scaling (Option A) requires defining step adjustments and thresholds. Simple scaling is no longer recommended by AWS. Scheduled scaling (Option B) is for predictable traffic patterns. Therefore, Option C is correct.

Answer

Step scaling

Answer

Scheduled scaling

Answer

Simple scaling

Question 17

A machine learning team needs to deploy a model that makes real-time predictions with latency under 100 ms. The model is a deep neural network with 500 MB of parameters. Which AWS service should they use?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are purpose-built for low-latency inference and can host large models like this 500 MB deep neural network by using appropriate instance types or multi-model endpoints. Option A (AWS Glue) is an ETL service, not for real-time inference. Option B (AWS Lambda) has a 250 MB deployment package limit and cold start latency that would exceed the 100 ms requirement for a 500 MB model. Option D (Amazon EMR) is designed for big data processing with Hadoop/Spark, not for real-time predictions. Therefore, the correct choice is Amazon SageMaker real-time endpoint.

Answer

AWS Glue

Answer

AWS Lambda with a container image

Answer

Amazon EMR

Question 18

A data engineer needs to continuously ingest streaming data from thousands of IoT devices and store the raw data in Amazon S3 for archival processing. The data volume varies significantly throughout the day, and the solution must be serverless, scalable, and cost-effective. Which AWS service should be used to capture and buffer the streaming data before writing to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service designed to reliably capture, buffer, and automatically load streaming data into Amazon S3 without requiring any custom code or infrastructure management. It handles variable data volumes by scaling automatically and provides built-in buffering (up to 128 MB or 900 seconds) before writing to S3, making it cost-effective for archival storage.

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)

Question 19

A company wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?

Accepted Answer

Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.. Combining millions of small JSON files into larger Parquet files using a Spark job on Amazon EMR is correct because it reduces the overhead of S3 LIST and GET requests during training. Parquet's columnar format also improves compression and allows SageMaker to read only the necessary columns, significantly accelerating I/O-bound training workloads.

Answer

Copy the data to an Amazon EBS volume attached to the training instance.

Answer

Use Amazon Athena to convert the data into a single CSV file.

Answer

Use S3 Select to filter data before training.

Question 20

A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job uses a large dataset stored in Amazon S3, and the scientist wants to use pipe mode to stream the data directly from S3 to the training instance, reducing the time needed to download the data. The training job is configured with 'InputMode' set to 'Pipe'. However, the training job fails with an error indicating that the algorithm does not support pipe mode. What should the scientist do to resolve this issue?

Accepted Answer

Change the 'InputMode' to 'File'. When a built-in algorithm does not support pipe mode, the simplest solution is to change the InputMode to 'File', which downloads the entire dataset before training. Option B is incorrect because pipe mode support depends on the algorithm, not the instance type. Option C is incorrect because AWS Glue is used for ETL and cannot directly stream data to a SageMaker training job. Option D is incorrect because while switching to an algorithm that supports pipe mode is possible, it may be unnecessary if the current algorithm works well with file mode, and changing the input mode is a simpler fix without altering the algorithm.

Answer

Use a different instance type that supports pipe mode

Answer

Use AWS Glue to stream the data to the training instance

Answer

Switch to a different built-in algorithm that supports pipe mode