MLS-C01 Practice Test 2 — 25 Questions

Question 1

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?

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

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

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Use Amazon ECS with AWS Fargate to run the processing task

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

Question 2

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

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SageMaker Model Monitor

Answer

SageMaker Experiments

Question 3

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 4

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 5

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

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Amazon RDS

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Amazon DynamoDB

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Amazon Athena

Question 6

A training job log shows this error. The training instance is an ml.m5.large with 8 GB EBS storage. The training data is 500 MB, and the model size is expected to be 200 MB. What is the most likely cause?

Accepted Answer

The training process is generating large temporary files that fill the instance's local storage. The training job runs on an ml.m5.large instance with only 8 GB of EBS storage. Given that the training data is 500 MB and the model size is 200 MB, there is ample space for those, but the error indicates a lack of storage space. This is most likely caused by temporary files (e.g., checkpoints, logs, or intermediate data) generated during training that fill up the remaining storage. Option A is incorrect because a download issue would show errors like 'Download failed' or 'NoSuchKey', not a storage error. Option B is incorrect because S3 permission issues would result in 'AccessDenied' errors. Option C is incorrect because insufficient RAM would cause a 'MemoryError' or out-of-memory kill, not a storage space error.

Answer

The training data is not fully downloaded from S3 before processing

Answer

The S3 bucket does not have write permissions

Answer

The training instance does not have enough RAM

Question 7

A financial services company is developing a fraud detection model using gradient boosting. The dataset contains 10 million transactions with 0.1% fraudulent. The model is trained on a SageMaker ml.m5.2xlarge instance and takes 8 hours. The team needs to reduce training time without sacrificing model performance. They have permission to use up to 4 instances. What should they do?

Accepted Answer

Switch to a built-in XGBoost with GPU support and use a p3.2xlarge instance. GPU instances like p3.2xlarge accelerate XGBoost training substantially.

Answer

Use SageMaker hyperparameter tuning to find faster hyperparameters

Answer

Use SageMaker's distributed training with data parallelism across 4 ml.m5.2xlarge instances

Answer

Use SageMaker managed spot training with checkpointing

Question 8

A machine learning engineer is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job fails with an AccessDenied error when trying to read the data. Which IAM policy addition should resolve the issue?

Accepted Answer

Add kms:Decrypt permission for the KMS key.. When an S3 bucket is encrypted with AWS KMS, the SageMaker training job's execution role must have the `kms:Decrypt` permission for the specific KMS key to read the encrypted objects. Without this permission, the job fails with an AccessDenied error, even if `s3:GetObject` is granted, because SageMaker must decrypt the data before reading it.

Answer

Add s3:GetObject permission for the bucket.

Answer

Add kms:GenerateDataKey permission for the key.

Answer

Attach the AmazonSageMakerFullAccess policy.

Question 9

A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing is the most suitable service because it is purpose-built for data preprocessing and feature engineering within the SageMaker ecosystem. It allows you to run custom Python scripts (e.g., using pandas or PySpark) on managed infrastructure to create aggregate features from support interactions and encode categorical variables, and it integrates seamlessly with SageMaker for model training and deployment.

Answer

AWS Glue

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Amazon EMR

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AWS Batch

Question 10

A company is building a data lake on Amazon S3 and wants to use AWS Glue to catalog the data. The data includes CSV, Parquet, and JSON files. The team wants to ensure that the Glue crawler can infer the schema correctly and update the Data Catalog when new partitions are added. Which crawler configuration should be used?

Accepted Answer

Create a single crawler that includes all file extensions and set the 'Update all new and existing partitions' option.. A single AWS Glue crawler can handle multiple file formats (CSV, Parquet, JSON) in a data lake on Amazon S3, and enabling 'Update all new and existing partitions' ensures the Data Catalog is refreshed with both new partitions and any schema changes in existing partitions. This configuration maintains a consistent and up-to-date catalog without manual intervention, which is essential for downstream analytics and machine learning workloads.

Answer

Create separate crawlers for each file format and schedule them at different times.

Answer

Use a crawler that only catalogs Parquet files because they are more efficient.

Answer

Use a crawler with 'Update all new and existing partitions' disabled to avoid schema conflicts.

Question 11

A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?

Accepted Answer

Add polynomial features (e.g., x^2, x^3) and check if model performance improves.. Options C, D, and E are correct. Adding polynomial features (e.g., x^2, x^3) allows a linear model to capture non-linear relationships. Decision tree regressors naturally model non-linear interactions between predictors and the target. A scatter plot with a LOESS smooth curve visually reveals non-linear patterns in the data. Option A (logistic regression) is incorrect because it is for binary classification, not for exploring non-linearity with a continuous target. Option B (Pearson correlation) only measures linear relationships, so it is not suitable for detecting non-linearity.

Answer

Apply logistic regression to binarize the target.

Answer

Compute the Pearson correlation coefficient between the predictor and target.

Question 12

Match each AWS AI service to its capability.

Accepted Answer

Amazon Comprehend: Extract insights and relationships from text. The correct matches are: Amazon Comprehend for extracting insights from text, Amazon Lex for building conversational interfaces, and Amazon Polly for text-to-speech. Distractors swap capabilities between services.

Answer

Amazon Lex: Translate text between languages

Answer

Amazon Polly: Convert speech to text

Question 13

A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class for all examples. Which metric should the scientist use to evaluate model performance more appropriately?

Accepted Answer

AUC-ROC. AUC-ROC is robust to class imbalance because it evaluates the model's ability to discriminate between positive and negative classes across all classification thresholds, rather than relying on a single threshold. In this scenario, the model predicts only the negative class, so its true positive rate is 0 and false positive rate is 0, yielding an AUC-ROC of 0.5 (random performance), which correctly reflects the model's lack of predictive power.

Answer

F1 score

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Mean squared error

Answer

Accuracy

Question 14

A company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink.. Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it provides a fully managed, serverless runtime for Apache Flink, which is designed for stateful stream processing at scale. It can aggregate millions of events per second with sub-second latency using exactly-once semantics and built-in checkpointing, meeting the near-real-time dashboard requirements without any infrastructure management.

Answer

AWS Lambda functions triggered by Kinesis Data Streams.

Answer

Amazon EMR with Spark Streaming.

Answer

Amazon Redshift with materialized views refreshed frequently.

Question 15

A company is building a data pipeline to process sensitive customer data. The pipeline uses AWS Glue for ETL and stores results in Amazon S3. The security team requires that all data be encrypted at rest in S3 using customer-managed AWS KMS keys. Additionally, the Glue job must be able to write encrypted data to S3. What should the data engineer do to meet these requirements?

Accepted Answer

Attach a policy to the Glue job's IAM role that includes kms:GenerateDataKey and kms:Decrypt actions for the KMS key.. AWS Glue jobs use an IAM role to interact with AWS services. To write encrypted data to S3 using a customer-managed AWS KMS key, the IAM role must have permissions for `kms:GenerateDataKey` (to request a data key for encryption) and `kms:Decrypt` (to decrypt the data key when reading or writing). This allows the Glue job to encrypt objects at rest in S3 with the specified KMS key, meeting the security team's requirement.

Answer

Use S3 server-side encryption with customer-provided keys (SSE-C).

Answer

Use S3 server-side encryption with SSE-S3, which is enabled by default.

Answer

Configure an S3 bucket policy to enforce encryption and attach it to the Glue job's IAM role.

Question 16

An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to Amazon S3. The data arrives at unpredictable rates, with occasional bursts. The company needs to ensure data is delivered within 60 seconds of ingestion, and the data must be partitioned by year/month/day/hour. Which configuration meets these requirements?

Accepted Answer

Set the buffer interval to 60 seconds and enable dynamic partitioning. Setting the buffer interval to 60 seconds ensures data is flushed to Amazon S3 within that time window, meeting the 60-second delivery requirement. Enabling dynamic partitioning allows Firehose to automatically partition data by year/month/day/hour based on the data's timestamp, without needing custom code or additional services.

Answer

Set the buffer size to 1 MB and disable dynamic partitioning

Answer

Use a Lambda function to process data and write to S3 with partitioning

Answer

Use AWS Glue streaming ETL to read from Firehose and write to S3

Question 17

A data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?

Accepted Answer

Use S3 object grouping or batch operations to combine small files before Glue processing.. The primary performance bottleneck with many small files in S3 is the overhead of listing, opening, and reading each file individually in Spark. By grouping or batching small files into larger objects (e.g., using S3 Batch Operations or a pre-processing step), you reduce the number of input splits and task launches, which dramatically lowers the cost and runtime of the Glue ETL job. This directly addresses the root cause of the inefficiency rather than merely scaling resources.

Answer

Increase the worker type to G.2X for more memory per worker.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Change the output format from Parquet to CSV to reduce compression overhead.

Question 18

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model currently achieves 99% accuracy but a recall of only 10% on the positive class. Which metric combination should the data scientist prioritize to evaluate model improvements?

Accepted Answer

F1 score and AUC-ROC. With a highly imbalanced dataset (1% positive class), 99% accuracy is misleading because the model can achieve it by simply predicting the majority class. The low recall (10%) indicates the model fails to identify most positive instances. The F1 score balances precision and recall, providing a single metric for minority class performance, while AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds, making it robust to class imbalance.

Answer

Precision and recall at 90% precision

Answer

Accuracy and RMSE

Answer

Precision and RMSE

Question 19

A data scientist needs to perform feature scaling for a dataset containing numerical features with different units (e.g., age in years and income in dollars). Which scaling method is most appropriate when the algorithm assumes data is normally distributed?

Accepted Answer

Standardization (Z-score normalization). Standardization (Z-score normalization) transforms features to have mean 0 and standard deviation 1, which is suitable for algorithms that assume normally distributed data. Option B (Log transformation) is used to reduce skewness, not for scaling when normality is already assumed. Option C (Min-Max scaling) scales to a fixed range (e.g., 0-1) but does not preserve normal distribution properties. Option D (Robust scaling) uses median and IQR, which is robust to outliers but does not assume normality.

Answer

Log transformation

Answer

Min-Max scaling

Answer

Robust scaling (using median and IQR)

Question 20

A data scientist is using AWS Glue to prepare training data. The job reads from an S3 bucket, performs transformations, and writes to another S3 bucket. The job is failing due to insufficient memory. Which solution should the data scientist use to fix this?

Accepted Answer

Increase the number of DPU (Data Processing Units) for the job.. The job is failing due to insufficient memory, which is a resource constraint. Increasing the number of DPU (Data Processing Units) allocates more memory and compute capacity to the AWS Glue job, directly addressing the out-of-memory error. This is the standard approach to scale Glue ETL jobs when they hit memory limits.

Answer

Use AWS Glue's job bookmark feature.

Answer

Use Amazon Athena instead of AWS Glue.

Answer

Use a columnar file format like Parquet.