MLS-C01 Practice Test 3 — 10 Questions

Question 1

A data scientist is using Amazon SageMaker to train a linear regression model. The target variable is right-skewed. Which transformation should the data scientist apply to the target variable to improve model performance?

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

Accepted Answer

Log transformation. (Log transformation) is correct because applying a logarithmic transformation to a right-skewed target variable can reduce skewness and make the distribution more normal, which improves the performance of linear regression models. Option A (Min-max scaling) scales the data to a fixed range but does not address skewness. Option B (One-hot encoding) is used for categorical variables, not for transforming continuous targets. Option D (PCA) is for dimensionality reduction, not for correcting skewness.

Answer

Min-max scaling

Answer

One-hot encoding

Answer

Principal Component Analysis (PCA)

Question 2

A healthcare company needs to predict patient readmission risk using clinical notes. Which AWS service can be used to preprocess the text data into numerical features for a machine learning model?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can extract entities, key phrases, and sentiment. It is suitable for preprocessing clinical notes into features. SageMaker Ground Truth is for data labeling. Rekognition is for images. Translate is for translation.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon Translate

Answer

Amazon Rekognition

Question 3

A data engineering team needs to process streaming data from thousands of IoT devices. The data must be ingested with low latency and processed in near real-time to detect anomalies. Which AWS service should they use for ingestion?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the correct choice because it is designed for real-time, low-latency ingestion of streaming data from thousands of sources, such as IoT devices. It provides a durable, scalable data stream that can be consumed by multiple applications in near real-time, making it ideal for anomaly detection use cases.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3

Question 4

A company is deploying a machine learning model for real-time fraud detection. The model must respond within 100ms. Which SageMaker endpoint deployment strategy should be used?

Accepted Answer

Deploy the model to a SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration.. A SageMaker Real-Time Inference endpoint with a Multi-Model Endpoint configuration provides low-latency (sub-100ms) responses by keeping models loaded in memory and routing requests efficiently. This architecture is ideal for real-time fraud detection where multiple models may be needed, and it meets the strict latency requirement without the cold-start overhead of serverless options.

Answer

Deploy the model to a SageMaker Serverless Inference endpoint.

Answer

Deploy the model as an AWS Lambda function with an API Gateway trigger.

Answer

Use SageMaker Batch Transform to process requests in batches.

Question 5

A company is building a recommender system using matrix factorization. The dataset contains user-item interactions. The model is trained on a large dataset, but the recommendations for new users are poor. Which approach would MOST effectively address this cold-start problem?

Accepted Answer

Incorporate user demographic features as side information. Matrix factorization models learn latent factors only from user-item interactions. For new users with no history, the model cannot compute a meaningful latent vector, leading to poor recommendations. Incorporating user demographic features as side information allows the model to initialize or infer latent factors for new users based on their attributes, directly addressing the cold-start problem.

Answer

Switch to item-based collaborative filtering only

Answer

Increase the number of latent factors in the model

Answer

Use only implicit feedback signals for training

Question 6

A data pipeline uses AWS Lambda to process small files (10-50 MB) from an S3 bucket and write results to DynamoDB. The Lambda function times out after 15 seconds for larger files. The team wants to handle files up to 100 MB without changing the Lambda code. Which solution is MOST cost-effective?

Accepted Answer

Increase the Lambda function timeout to 5 minutes. Increasing the Lambda function timeout to 5 minutes directly addresses the 15-second timeout issue for larger files (up to 100 MB) without requiring any code changes. This is the most cost-effective solution as it avoids additional infrastructure costs (e.g., Glue, ECS, SQS) and leverages Lambda's existing pay-per-execution pricing model, which remains economical for occasional longer-running invocations.

Answer

Use AWS Glue Python shell job to replace Lambda

Answer

Use Amazon ECS with AWS Fargate to run the processing task

Answer

Configure an SQS queue to buffer the S3 events and batch them

Question 7

A data scientist wants to automate the selection of optimal hyperparameters for a model. Which SageMaker feature should be used?

Accepted Answer

SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning (AMT) is the correct feature because it automates hyperparameter optimization by running multiple training jobs with different hyperparameter combinations, using algorithms like Bayesian optimization or random search to find the best set. This directly addresses the requirement to automate selection of optimal hyperparameters.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Question 8

A data scientist is analyzing a dataset with high multicollinearity. Which TWO techniques can help identify and address multicollinearity?

Accepted Answer

Use Principal Component Analysis (PCA). Correct options: D and E. Variance Inflation Factor (VIF) (E) is a key metric for detecting multicollinearity by measuring how much the variance of a coefficient increases due to collinearity. PCA (D) addresses multicollinearity by transforming correlated features into orthogonal components. Option A is incorrect because a correlation matrix only shows pairwise correlations and may miss higher-order multicollinearity. Option B is incorrect because Lasso regression performs feature selection by shrinking coefficients but does not directly identify multicollinearity. Option C is incorrect because Recursive Feature Elimination (RFE) is a feature selection method that does not detect multicollinearity.

Answer

Plot a correlation matrix

Answer

Apply Lasso regression

Answer

Use Recursive Feature Elimination (RFE)

Question 9

A data engineer is building a data pipeline to process user clickstream data. The data arrives as JSON files in an S3 bucket. The pipeline must transform the JSON into Parquet format and partition by date and event type, then make the data available for Amazon Athena queries. The engineer needs a fully managed, serverless solution with minimal operational overhead. Which combination of AWS services should the engineer use?

Accepted Answer

Use S3 Event Notifications to trigger an AWS Lambda function that converts the JSON to Parquet and writes to a partitioned S3 location, then query with Athena.. AWS Lambda triggered by S3 Event Notifications provides a fully serverless, event-driven architecture with minimal operational overhead for converting JSON to Parquet and partitioning by date and event type. Lambda can process each new JSON file as it arrives, perform the transformation in memory (using libraries like PyArrow or Pandas), and write the Parquet output to a partitioned S3 path, which Athena can then query directly. This approach avoids managing any clusters or job scheduling, aligning with the requirement for a fully managed, serverless solution.

Answer

Use Amazon EMR with Spark to read JSON, convert to Parquet, and partition, then query with Athena.

Answer

Use AWS Glue ETL jobs to read JSON from S3, transform to Parquet, and write to a partitioned S3 location, then use Athena.

Answer

Use Amazon Kinesis Firehose to ingest data and convert to Parquet, then write to S3, and query with Athena.

Question 10

A data engineering team is designing a data lake on AWS. They need to store raw data in S3 and allow multiple analytics services to query the data. Which service can be used to catalog and provide schema information for the data?

Accepted Answer

AWS Glue Data Catalog. AWS Glue Data Catalog is a fully managed metadata repository that stores table definitions, schema information, and partition details for data in S3. Amazon Athena, while it can query data in S3 using SQL, does not provide its own catalog; it relies on the Glue Data Catalog for schema information. Therefore, only AWS Glue Data Catalog directly catalogs and provides schema information.

Answer

Amazon Kinesis Data Streams

Answer

Amazon RDS

Answer

Amazon DynamoDB

Answer

Amazon Athena