MLS-C01 Practice Test 33 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has outliers. Which TWO techniques can help reduce the impact of outliers? (Choose TWO.)

Accepted Answer

Trim the dataset to remove extreme values. Options A and D are correct. Huber loss is robust to outliers, and trimming the dataset removes extreme values. Option B (more features) is not relevant for handling outliers. Option C (L1 regularization) reduces overfitting but not outlier impact. Option E (standardization) does not handle outliers.

Answer

Add more features

Answer

Apply L1 regularization

Answer

Standardize the features

Question 2

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has outliers. Which TWO techniques can help reduce the impact of outliers? (Choose TWO.)

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

Accepted Answer

Apply feature hashing to UserID.. Hashing reduces the number of distinct categories to a fixed number of buckets, controlling dimensionality. Option A is wrong because one-hot encoding would explode the feature space. Option C is wrong because label encoding creates ordinal relationships that may mislead the model. Option D is wrong because removing UserID likely loses important signal.

Answer

Use one-hot encoding on UserID.

Answer

Use label encoding for UserID.

Answer

Remove UserID from the dataset.

Question 3

A company is using AWS Glue to run ETL jobs that process data in an S3 data lake. The jobs are failing with out-of-memory errors when processing large files. Which configuration change should be made to resolve this issue?

Accepted Answer

Increase the number of DPUs allocated to the job. Out-of-memory errors in AWS Glue ETL jobs indicate that the allocated memory (DPUs) is insufficient for the data being processed. Increasing the number of DPUs allocates more memory and compute capacity to the job, directly resolving the memory constraint. This is the standard approach for scaling Glue jobs handling large datasets.

Answer

Change the worker type to G.1X

Answer

Partition the input data into smaller files

Answer

Enable job bookmark to process only new data

Question 4

A company is designing a data pipeline that ingests streaming data from social media feeds. The data must be processed in real-time to detect trending topics, and results must be stored in Amazon DynamoDB for low-latency access. Which services should the company use? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (D) is correct because it provides real-time SQL-based processing of streaming data, enabling the detection of trending topics from social media feeds without requiring custom code. It directly analyzes data from Kinesis Data Streams and can output results to DynamoDB via a Lambda function or Firehose, meeting the low-latency storage requirement.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Lambda

Answer

Amazon Simple Queue Service (SQS)

Question 5

A company is building a sentiment analysis model for customer reviews. The dataset is balanced with 10,000 positive and 10,000 negative reviews. The model achieves 95% accuracy on the test set but fails to generalize to new reviews from a different product category. Which TWO techniques can improve generalization?

Accepted Answer

Use stratified k-fold cross-validation during training. Stratified k-fold cross-validation ensures that each fold maintains the same class distribution as the original dataset, which helps the model learn more robust patterns across different subsets of data. This technique reduces variance in the evaluation and improves generalization to unseen data from different product categories by preventing overfitting to idiosyncrasies of a single train-test split.

Answer

Increase the training dataset size by collecting more reviews

Answer

Add more features like review length and word count

Answer

Use a more complex model with more layers

Question 6

A company is streaming real-time sensor data from IoT devices to Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that enriches the records with metadata from an Amazon DynamoDB table and writes the results to an Amazon S3 bucket. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' errors from DynamoDB. The data volume is variable, with occasional bursts. Which solution should a data engineer implement to resolve this issue without losing data?

Accepted Answer

Enable DynamoDB auto scaling for the table to automatically adjust read capacity based on demand.. DynamoDB auto scaling dynamically adjusts the table's provisioned read capacity based on actual traffic patterns, handling bursty sensor data without manual intervention. This prevents ProvisionedThroughputExceededExceptions while ensuring no data loss, as the Lambda function can retry failed operations. Auto scaling is the most cost-effective and operationally efficient solution for variable workloads.

Answer

Increase the DynamoDB table's provisioned read capacity units to a high static value.

Answer

Use an Amazon SQS queue to buffer the Lambda requests before querying DynamoDB.

Answer

Configure an Amazon SNS topic to throttle the data stream before it reaches Lambda.

Question 7

A data engineering team is building a pipeline to process terabytes of log data daily using Amazon EMR with Spark. The data arrives in hourly batches and must be processed within 4 hours. The team needs to minimize cost. Which cluster configuration is MOST cost-effective?

Accepted Answer

Use a transient cluster with a mix of on-demand and spot instances, terminated after the job completes.. A transient cluster with a mix of on-demand and spot instances minimizes cost for batch workloads that have a defined lifecycle. Spot instances offer significant discounts (up to 90%) for fault-tolerant Spark jobs, and terminating the cluster after processing eliminates idle compute charges. This approach aligns with the 4-hour processing window and hourly batch arrival, as EMR can provision and tear down clusters quickly.

Answer

Use a single large instance with multiple cores to avoid data shuffling.

Answer

Use a long-running cluster of on-demand instances to avoid startup time.

Answer

Use Amazon EMR Serverless to automatically scale.

Question 8

A data scientist is working with a dataset containing text reviews. The goal is to build a sentiment analysis model. Which EDA step is most critical before feature extraction?

Accepted Answer

Checking the distribution of sentiment labels. Checking the distribution of sentiment labels is critical before feature extraction because it reveals class imbalance, which can bias the model towards the majority class and affect evaluation metrics. This EDA step enables informed decisions about resampling or weighting techniques. Option A (vocabulary size) is not a critical first step; option B (word cloud) is a visualization tool, not essential; option C (removing stop words) is a preprocessing step, not part of EDA.

Answer

Calculating the vocabulary size

Answer

Creating a word cloud

Answer

Removing stop words

Question 9

A data scientist is analyzing a dataset with a timestamp column. The goal is to identify seasonality and trends. Which visualization technique is most suitable?

Accepted Answer

Time series line plot of the target variable over time.. A time series line plot is the standard visualization for identifying trends and seasonality over time. Option B (box plot grouped by day of week) can show distributions but may not reveal trends or seasonality clearly. Option C (scatter plot of target vs. timestamp) can show patterns but may be less clear than a line plot for time series. Option D (heatmap of correlations) is for exploring relationships between features, not for time series analysis.

Answer

Box plot of the target variable grouped by day of week.

Answer

Scatter plot of the target variable vs. the timestamp.

Answer

Heatmap of correlation between all features.

Question 10

A data engineer is designing a data pipeline that uses Amazon S3 events to trigger an AWS Lambda function for processing. The pipeline must handle high throughput with low latency. Which TWO configurations should be applied?

Accepted Answer

Configure Lambda with reserved concurrency. Reserved concurrency ensures that the Lambda function always has a guaranteed number of concurrent executions available, preventing it from being throttled by other functions in the same AWS account. This is critical for high-throughput, low-latency pipelines because S3 event notifications can burst many invocations simultaneously, and without reserved concurrency, the function might hit the account-level concurrency limit and drop events.

Answer

Use an SQS queue between S3 and Lambda

Answer

Place Lambda in a VPC to reduce network latency

Answer

Use Amazon Kinesis Data Streams as an intermediary

Question 11

A financial services company is building a fraud detection model that requires joining real-time transaction data with a reference dataset of known fraudulent accounts stored in Amazon DynamoDB. The solution must minimize latency and be highly available. The reference dataset is updated frequently (every few minutes). Which architecture should the team use?

Accepted Answer

Use Amazon Kinesis Data Analytics to process the stream and join with a DynamoDB table.. Amazon Kinesis Data Analytics (now managed Apache Flink) can directly reference a DynamoDB table as a reference source via the Flink Table API or SQL JOINs, enabling low-latency, stateful stream enrichment without external query overhead. This architecture minimizes latency by performing the join in-memory within the streaming application, and it supports high availability through Kinesis Data Analytics' automatic checkpointing and failover.

Answer

Use Amazon Athena to query the DynamoDB table and join with streaming data.

Answer

Use AWS Glue streaming ETL to read from Kinesis and join with DynamoDB.

Answer

Use Amazon SageMaker to host a model that queries DynamoDB for each inference.

Question 12

A data engineer is configuring an IAM policy to allow users to upload objects to an S3 bucket only if the objects are encrypted using SSE-S3. However, users are getting AccessDenied errors when uploading objects without specifying encryption. What is the most likely cause?

Accepted Answer

The condition requires encryption to be specified, but the upload does not specify it. The policy allows PutObject only when encryption is AES256, but denies when no encryption is specified because the condition is not met. Option A is wrong because it's not a service control policy; Option C is wrong because the bucket policy is not shown; Option D is wrong because the condition checks for AES256, not KMS.

Answer

The condition should check for aws:SourceIp instead of encryption

Answer

The policy is attached to the wrong IAM user

Answer

The bucket policy denies all PutObject without encryption

Question 13

A data scientist needs to transform raw JSON data from an S3 bucket into Parquet format using AWS Glue. The job must be cost-effective and run only when new data arrives. Which solution should be used?

Accepted Answer

Create a Glue ETL job triggered by an S3 event notification via Lambda.. It uses an S3 event notification to invoke a Lambda function, which then triggers an AWS Glue ETL job only when new data arrives. This event-driven architecture ensures cost-effectiveness by avoiding continuous or scheduled runs, and it directly transforms raw JSON into Parquet format as required.

Answer

Create a Glue crawler that runs continuously.

Answer

Schedule a Glue ETL job to run every hour.

Answer

Use Glue DataBrew to transform data and schedule it daily.

Question 14

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with timeouts. What is the most likely cause?

Accepted Answer

The AWS Glue job does not have enough DPUs (Data Processing Units) allocated.. Intermittent timeouts in AWS Glue ETL jobs typically indicate insufficient resource allocation. DPUs (Data Processing Units) define the compute capacity for the job; if too few are allocated, the job may run slowly and exceed the default timeout (e.g., 2880 minutes) or internal service limits, especially when processing large datasets from S3 to Redshift. Increasing the DPU count or using the G.1X/G.2X worker types can resolve this.

Answer

The S3 bucket policy is too restrictive.

Answer

The Amazon Redshift cluster is in maintenance mode.

Answer

The source data is not compressed.

Question 15

A data scientist is using SageMaker to train a linear regression model. The target variable has a long-tail distribution. Which data transformation is LEAST likely to improve model performance?

Accepted Answer

Normalize all feature values to [0,1]. Normalizing feature values to [0,1] is a scaling technique that does not address the long-tail distribution of the target variable. Long-tail distributions typically require transformations that compress the tail (e.g., log or Box-Cox) to make the relationship more linear and reduce the influence of extreme values. Feature normalization helps with gradient descent convergence but does not fix skewness in the target, so it is least likely to improve model performance for this specific issue.

Answer

Add interaction terms between features

Answer

Apply log transformation to the target variable

Answer

Remove outliers from the target variable