MLS-C01 Practice Exam 54 — 20 Questions

Question 1

A data scientist is exploring a dataset with a column 'transaction_date'. They want to create features for day of week and month. What is the correct AWS service to schedule a recurring ETL job for this transformation?

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

Accepted Answer

AWS Glue. AWS Glue is a serverless ETL service that can be scheduled to run recurring jobs for data transformation, such as extracting day of week and month from a date column. Option A is wrong because Amazon Athena is an interactive query service, not an ETL scheduler. Option C is wrong because Amazon SageMaker is a machine learning platform, not an ETL service. Option D is wrong because AWS Lambda can run code on a schedule but is not a full-fledged ETL service for recurring transformations of large datasets.

Answer

Amazon Athena

Answer

Amazon SageMaker

Answer

AWS Lambda

Question 2

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is JSON and must be partitioned by year, month, and day. The delivery stream is configured with a buffer interval of 60 seconds and buffer size of 5 MB. The data producer sends about 1 MB per second. The data is arriving in S3 but the partitions are not being created as expected. What is the MOST likely reason?

Accepted Answer

The delivery stream does not have dynamic partitioning enabled with the appropriate custom prefix.. Kinesis Data Firehose requires dynamic partitioning to be explicitly enabled and configured with a custom prefix (e.g., 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/') to automatically partition data by year, month, and day. Without this setting, Firehose writes all data to a single S3 prefix, ignoring the desired partition structure.

Answer

The data is encrypted with AWS KMS and Firehose cannot write to encrypted buckets.

Answer

The buffer interval is too short for the data volume, causing incomplete records.

Answer

The S3 bucket has versioning enabled, which prevents partitioning.

Question 3

A financial services company is developing a fraud detection model using a highly imbalanced dataset where fraudulent transactions are only 0.1% of the data. The data scientist has trained a gradient boosting model that achieves 99.9% accuracy but only detects 20% of actual fraud cases. The business requirement is to detect at least 80% of fraud while minimizing false positives. The data scientist has access to SageMaker and can use any built-in algorithm or custom script. Which approach should the data scientist take to meet the business requirement?

Accepted Answer

Use Amazon SageMaker Random Cut Forest (RCF) algorithm for anomaly detection.. Amazon SageMaker Random Cut Forest (RCF) is an unsupervised anomaly detection algorithm that is well-suited for highly imbalanced datasets like this one (0.1% fraud). Unlike supervised methods that struggle with extreme class imbalance, RCF isolates anomalies by measuring how many random cuts are needed to separate a point from the rest of the data, making it effective at detecting rare fraud cases without requiring balanced training data. This approach can meet the 80% fraud detection requirement while minimizing false positives by tuning the anomaly score threshold.

Answer

Keep the model but adjust the classification threshold to increase recall.

Answer

Use random under-sampling of the majority class to balance the dataset and retrain the model.

Answer

Use random oversampling of the minority class to balance the dataset and retrain the model.

Question 4

A data engineer needs to schedule an AWS Glue ETL job to run every hour. The job reads from an Amazon DynamoDB table and writes to Amazon S3. Which AWS service should the engineer use to trigger the Glue job on schedule?

Accepted Answer

Amazon EventBridge (CloudWatch Events). Amazon EventBridge (formerly CloudWatch Events) can trigger AWS Glue jobs on a schedule using cron or rate expressions. Option A is incorrect because Amazon Kinesis Data Streams is for real-time streaming data, not scheduling. Option B is incorrect because AWS Step Functions is for orchestrating workflows, but scheduling is typically done via EventBridge. Option D is incorrect because AWS Lambda is a compute service, not a scheduler, though it can be used in conjunction with EventBridge to trigger Glue, but the direct scheduler is EventBridge.

Answer

Amazon Kinesis Data Streams

Answer

AWS Step Functions

Answer

AWS Lambda

Question 5

A machine learning pipeline uses SageMaker Processing jobs for feature engineering. Which TWO are benefits of using SageMaker Processing over running a custom script on an EC2 instance?

Accepted Answer

Automatically manages the compute resources. SageMaker Processing automatically manages the underlying compute resources, including provisioning, scaling, and terminating instances. This eliminates the need for manual infrastructure management, which is required when running a custom script on an EC2 instance. Option B is correct because SageMaker Processing jobs natively integrate with SageMaker Experiments, allowing automatic tracking of parameters, metrics, and artifacts for reproducibility and comparison.

Answer

Provides a built-in VPC for network isolation

Answer

Allows use of custom Docker images from any registry

Answer

Supports multiple programming languages

Question 6

A data scientist runs the following AWS CLI command:
aws s3api head-object --bucket my-bucket --key data.csv

The output is:
{
    "AcceptRanges": "bytes",
    "LastModified": "2021-08-21T12:00:00+00:00",
    "ContentLength": 1048576,
    "ETag": "\"d41d8cd98f00b204e9800998ecf8427e\"",
    "ContentType": "text/csv",
    "Metadata": {}
}

What can be concluded from the output?

Accepted Answer

The ETag can be used for integrity checking.. The ETag can be used for integrity checking, as it is an MD5 hash of the object content. Option B is wrong because ContentLength 1048576 corresponds to 1 MB, not 1 GB. Option C is wrong because S3 versioning is indicated by VersionId, not ETag. Option D is wrong because the output does not provide any information about preprocessing; the presence of metadata like 'preprocessed' is not shown.

Answer

The file is 1 GB in size.

Answer

The object has S3 versioning enabled.

Answer

The file has not been preprocessed.

Question 7

An S3 event notification triggers an AWS Lambda function when a new object is created. The Lambda function parses the event and processes the object. The function is failing with a timeout error for large objects. Which approach should be used to handle large objects efficiently?

Accepted Answer

Increase the Lambda function timeout to 15 minutes. Increasing the Lambda function timeout to the maximum of 15 minutes directly addresses the timeout error for large objects. This allows the function to complete processing within the allotted time. Option B (SQS queue) does not solve the timeout issue because the processing time itself is too long; buffering events does not reduce processing time. Option C (Kinesis) adds unnecessary streaming complexity. Option D (Step Functions) adds orchestration overhead without addressing the timeout.

Answer

Use an SQS queue to buffer event notifications and configure Lambda with a batch window

Answer

Stream events to Amazon Kinesis Data Streams and process with Lambda

Answer

Use AWS Step Functions to orchestrate the processing

Question 8

A data scientist is analyzing a dataset with many categorical features. The target variable is binary. Which statistical test should be used to assess the association between each categorical feature and the target?

Accepted Answer

Chi-squared test of independence. The Chi-squared test of independence is the appropriate test to assess association between two categorical variables. Here, both the features (categorical) and the target (binary, which is categorical) are categorical, making the Chi-squared test the correct choice. Option A (Pearson correlation) is for continuous variables, not categorical. Option C (ANOVA) compares means across groups for a continuous target, but our target is binary (categorical). Option D (Kolmogorov-Smirnov test) compares distributions of continuous variables, not categorical. Therefore, B is correct.

Answer

Pearson correlation coefficient

Answer

ANOVA

Answer

Kolmogorov-Smirnov test

Question 9

A data engineer is preparing a dataset for training a binary classification model. The target variable is highly imbalanced (95% negative, 5% positive). The engineer needs to split the data into training and test sets while maintaining the class distribution in both sets. Which method should the engineer use?

Accepted Answer

Use stratified random sampling to split the data. Stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because k-fold cross-validation is a model evaluation technique, not a method for splitting data into training and test sets; using it before splitting would not guarantee class balance. Option B is wrong because oversampling should be done after splitting to avoid data leakage and ensure the test set reflects the original distribution. Option C is wrong because a simple random 80/20 split may not preserve the class distribution due to random variation, especially with imbalanced data.

Answer

Use k-fold cross-validation and then split the data

Answer

Oversample the minority class first, then do a random split

Answer

Perform a simple random 80/20 split

Question 10

A data scientist is building a fraud detection model using a dataset of 500,000 credit card transactions. The dataset contains 20 features, including transaction amount, merchant category, time since last transaction, and customer age. The target variable 'is_fraud' has 0.1% positive examples. Initial EDA reveals that the transaction amount distribution is highly skewed with a long tail. Also, there are missing values in the 'customer_age' field (5% missing). The data scientist needs to prepare the data for training a binary classifier. Which combination of preprocessing steps should the data scientist apply to address these issues and improve model performance? (Select TWO.)

Accepted Answer

Apply log transformation to the transaction amount to reduce skewness.. This is a multi-select question requiring two correct preprocessing steps. Option C is correct because applying a log transformation to the highly skewed transaction amount reduces skewness and compresses the dynamic range, which helps many machine learning algorithms (especially those sensitive to feature scales like logistic regression or SVM) converge faster and perform better. Option D is correct because imputing missing values in customer_age with the mean is a simple and effective method when the missing rate is only 5% and the data is roughly normally distributed, preserving sample size. Option A is wrong because SMOTE is typically applied after splitting the data to avoid data leakage, and it is not a preprocessing step for EDA; also, the class imbalance is severe but SMOTE may be considered later. Option B is wrong because standard scaling does not handle skewness; it should be applied after skewness correction. Option E is wrong because dropping the feature due to skewness would lose valuable information; transformation is preferable.

Answer

Use SMOTE to generate synthetic samples of the minority class.

Answer

Apply standard scaling to all numerical features.

Answer

Drop the transaction amount feature because of its skewness.

Question 11

A data scientist is analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method is MOST appropriate?

Accepted Answer

Multiple imputation by chained equations (MICE). MICE uses multiple imputation based on other features, accounting for the correlation between the missing feature and another feature. Option A is wrong because median imputation ignores the correlation and simply fills with the median, which does not leverage relationships between features. Option B is wrong because listwise deletion removes rows with missing data, which reduces sample size and can introduce bias if missingness is not completely random. Option C is wrong because mean imputation, like median imputation, ignores correlations and can distort relationships.

Answer

Median imputation

Answer

Listwise deletion (remove rows with missing values)

Answer

Mean imputation

Question 12

A data engineer is troubleshooting an AWS Glue job that reads from an S3 bucket and writes to another S3 bucket. The job fails with an 'Access Denied' error when trying to write to the output bucket. The IAM policy attached to the Glue service role is shown. What is the MOST likely cause of the failure?

Accepted Answer

The output bucket is not listed in the Resource of the IAM policy. The IAM policy attached to the Glue service role explicitly lists the output bucket in the Resource field. If the output bucket is not listed, the Glue job will receive an 'Access Denied' error when attempting to write to it, because the policy does not grant the necessary s3:PutObject permission for that bucket. This is the most direct cause of the failure.

Answer

The user who runs the job does not have S3 permissions

Answer

The Glue job role does not have permissions to start a job run

Answer

The S3 bucket policy denies access to the Glue service

Question 13

A data engineer is building a data pipeline that aggregates customer transaction data. The engineer notices that some transactions have duplicate entries due to a system error. Which approach should the engineer use to identify and remove duplicates based on a unique transaction ID?

Accepted Answer

Use the drop_duplicates method on the transaction ID column. Using drop_duplicates on the transaction ID column is a straightforward and efficient method to remove duplicate rows based on the unique identifier. Option A is incorrect; sorting and checking consecutive rows is a valid but more complex approach, and not as direct as drop_duplicates. Option B is incorrect because fuzzy matching is designed for approximate matches, not exact duplicates. Option C is incorrect because grouping by all columns and summing would aggregate data, potentially losing information, and does not specifically remove duplicate transaction IDs.

Answer

Sort the data by transaction ID and then check consecutive rows for equality

Answer

Use fuzzy matching to find similar transaction IDs

Answer

Group by all columns and aggregate with sum

Question 14

A data scientist is building a regression model to predict house prices. The dataset contains many features, some of which are highly correlated. The model is overfitting. Which regularization technique should the scientist use to penalize large coefficients and perform feature selection?

Accepted Answer

L1 regularization (Lasso). L1 regularization (Lasso) adds a penalty equal to the absolute value of the coefficients, which can shrink some coefficients to zero, performing feature selection. L2 regularization (Ridge) penalizes squared coefficients but does not zero them out. Elastic Net combines both. Dropout is for neural networks. Option A (L2 regularization) does not perform feature selection. Option B (L1 regularization) is correct for feature selection. Option C (Elastic Net) combines both but L1 alone is simpler for feature selection. Option D (Dropout) is not applicable to linear regression.

Answer

L2 regularization (Ridge)

Answer

Elastic Net regularization

Answer

Dropout

Question 15

A company is using Amazon SageMaker to train a model. Which TWO metrics should be used to evaluate a binary classification model?

Accepted Answer

AUC. AUC (Area Under the ROC Curve) is a threshold-independent metric that measures the model's ability to distinguish between positive and negative classes across all classification thresholds. For binary classification in SageMaker, AUC is robust to class imbalance and provides a single scalar value representing overall model performance, making it a standard evaluation metric.

Answer

Accuracy

Answer

Perplexity

Answer

Mean Absolute Error

Question 16

A data scientist is building a model to predict insurance claim amounts. The target variable is right-skewed with many small claims and a few very large claims. The scientist wants to minimize the impact of outliers. Which loss function or transformation is MOST appropriate?

Accepted Answer

Apply a log transformation to the target variable. Applying a log transformation to the target variable reduces skewness and mitigates the impact of outliers by compressing the scale of large values. This makes the distribution more symmetric and suitable for models like linear regression. Option A (mean squared error) is sensitive to outliers. Option B (quantile loss) predicts the median, which is robust but not typical for mean prediction. Option C (Poisson loss) is designed for count data, not continuous skewed targets. Option D (log transformation) is the standard approach for right-skewed continuous targets.

Answer

Use mean squared error loss without any transformation

Answer

Use quantile loss to predict the median

Answer

Use Poisson loss assuming the target follows a Poisson distribution

Question 17

Refer to the exhibit. A data engineer is creating an IAM policy for an AWS Glue ETL job that reads encrypted objects from an S3 bucket, transforms them, and writes the results back to the same bucket. The bucket uses SSE-KMS encryption with the KMS key specified. The ETL job is failing with an "Access Denied" error when trying to write data. What is the likely cause?

Accepted Answer

The policy is missing the kms:Encrypt permission. The IAM policy must include the kms:Encrypt permission for the AWS Glue ETL job to write encrypted objects to the S3 bucket using SSE-KMS. The policy likely includes s3:PutObject, kms:Decrypt, and kms:GenerateDataKey, but kms:Encrypt is required for the write operation. Options A, B, and C are incorrect because the necessary permissions (kms:Decrypt, s3:PutObjectAcl, and s3:PutObject) are either already present or not required for writing.

Answer

The policy is missing the kms:Decrypt permission

Answer

The policy is missing the s3:PutObjectAcl permission

Answer

The policy is missing the s3:PutObject permission

Question 18

A SageMaker endpoint creation fails with the above CloudWatch Logs excerpt. What is the MOST likely cause?

Accepted Answer

The model file is too large and takes longer than 300 seconds to load. The CloudWatch Logs excerpt shows a timeout error during model loading. SageMaker has a default 300-second timeout for downloading and loading model artifacts from S3 into the inference container. If the model file is too large or the network is slow, the container fails to start within this window, causing the endpoint creation to fail with a timeout error.

Answer

The S3 bucket containing the model artifacts has incorrect permissions

Answer

The inference script has a syntax error

Answer

The instance type does not have enough memory to load the model

Question 19

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset has missing values in the 'age' column and outliers in the 'amount' column. Which combination of techniques should the engineer use to handle these issues during EDA?

Accepted Answer

Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.. Median imputation is robust to outliers, and IQR-based capping is a standard method for handling outliers. Option B is wrong because removing rows with missing age can lead to data loss, and log transformation reduces skewness but does not handle outliers by capping. Option C is wrong because imputing with a constant like 0 is arbitrary and can bias the data, and capping using mean ± 3*std is sensitive to outliers. Option D is wrong because mean imputation is sensitive to outliers, and removing outliers via z-score can discard valid data points.

Answer

Remove rows with missing age and apply log transformation to 'amount'.

Answer

Impute missing age values with a constant (e.g., 0) and cap outliers using mean ± 3*std.

Answer

Impute missing age values with the mean and remove outliers in 'amount' using z-score.

Question 20

A data scientist is assigned an IAM policy as shown. The data scientist attempts to create a SageMaker endpoint to deploy a model, but the request fails. What is the most likely reason?

Accepted Answer

The data scientist does not have permission to create an endpoint.. The IAM policy shown does not include the `sagemaker:CreateEndpoint` action, which is required to create a SageMaker endpoint. Even if the data scientist has permissions for other SageMaker actions like `CreateModel` or `CreateEndpointConfig`, the explicit absence of `CreateEndpoint` in the policy will cause the request to fail with an access denied error. AWS IAM policies must explicitly grant each action needed for the operation.

Answer

The data scientist does not have permission to upload the model to S3.

Answer

The data scientist does not have permission to create a training job.

Answer

The data scientist does not have permission to pass roles.