MLS-C01 Practice Exam 67 — 20 Questions

Question 1

A data scientist is starting a new machine learning project and needs to understand the dataset. The dataset is stored as CSV files in Amazon S3, with a total size of 50 GB. The data scientist wants to quickly get summary statistics (count, mean, standard deviation, min, max) for each numerical column, and also check for missing values. The data scientist has access to SageMaker Studio. What is the most efficient way to achieve this?

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

Accepted Answer

Use SageMaker Data Wrangler to import the data and generate a data quality report.. SageMaker Data Wrangler is purpose-built for data preparation and profiling, allowing you to compute summary statistics and check for missing values with a visual interface and without writing code. Option A (AWS Glue Crawler + Athena) only infers schema and enables SQL queries; it does not automatically provide summary statistics or missing value counts. Option B (PySpark script) is possible but requires manual coding and Spark cluster management, making it less efficient for quick exploration. Option C (Amazon QuickSight) is a BI tool that requires loading data into SPICE, which is not as streamlined for initial data profiling as Data Wrangler.

Answer

Use AWS Glue Crawler to infer schema and then query with Athena.

Answer

Write a PySpark script in a SageMaker notebook to compute statistics.

Answer

Load a sample into Amazon QuickSight and use SPICE to compute statistics.

Question 2

A data scientist is exploring a dataset with 200 features. They compute the pairwise correlation matrix and notice that many features have correlations above 0.95. They want to reduce redundancy before modeling. Which of the following techniques is most appropriate for identifying and removing highly correlated features?

Accepted Answer

Perform hierarchical clustering on the correlation matrix and select one feature per cluster.. Hierarchical clustering on correlations groups correlated features; then one can select a representative from each cluster. Option A is wrong because mutual information with the target does not capture pairwise redundancy among features. Option B is wrong because PCA creates new features but does not remove original ones. Option C is wrong because Lasso regression performs feature selection but may not handle multicollinearity well and does not directly identify redundant groups.

Answer

Compute mutual information between each feature and the target.

Answer

Apply PCA and keep the first 50 components.

Answer

Use Lasso regression to select features.

Question 3

A machine learning team is analyzing a dataset with numerical features. They compute the pairwise correlation matrix and find that two features, 'X1' and 'X2', have a correlation coefficient of 0.98. The team plans to train a linear regression model. Which of the following actions should the team take to avoid multicollinearity issues?

Accepted Answer

Remove one of the two highly correlated features.. Removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA creates new uncorrelated features but is not necessary for just two correlated features. Option B is wrong because adding an interaction term between X1 and X2 would actually increase multicollinearity. Option C is wrong because standard scaling does not address correlation between features.

Answer

Perform PCA on the dataset to reduce dimensionality.

Answer

Add an interaction term between X1 and X2 to the model.

Answer

Standardize both features using Z-score normalization.

Question 4

A data engineer needs to transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?

Accepted Answer

Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.. AWS Glue ETL jobs are serverless, require no cluster management, and can be easily scheduled for daily runs. Glue also integrates with the Data Catalog for partitioning. Option A (Amazon EMR) is not the most cost-effective or least operational overhead because it requires managing a cluster, even if it can be terminated after completion. Option C (AWS Lambda) is not suitable for large-scale clickstream data due to execution time limits and lack of built-in support for complex transformations like deduplication. Option D (Amazon Athena CTAS) is not appropriate because Athena is primarily a query engine, not a transformation tool; CTAS queries are good for converting data formats but lack the flexibility for cleaning and enrichment.

Answer

Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.

Answer

Use AWS Lambda functions triggered by S3 events to transform each file incrementally.

Answer

Use Amazon Athena to run CTAS queries to convert and partition the data daily.

Question 5

A machine learning engineer is examining a dataset containing text reviews. They want to convert the text into numerical features for a model. During EDA, they notice that the word 'the' appears in almost every review, while words like 'excellent' appear rarely. Which of the following techniques should they use to reduce the impact of very common words?

Accepted Answer

Apply TF-IDF transformation.. TF-IDF transformation downweights common words (like 'the') and emphasizes rare but informative words (like 'excellent'). Option B (removing stopwords) is insufficient because it does not adjust for frequency beyond removing a predefined list; TF-IDF handles frequency weighting. Option C (word2vec embeddings) captures semantic relationships but does not specifically reduce the impact of common words. Option D (bag-of-words) does not perform any weighting, so common words dominate.

Answer

Remove stopwords from the text.

Answer

Use word2vec embeddings.

Answer

Use a bag-of-words representation.

Question 6

Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?

Accepted Answer

Check the proportion of missing values for each feature.. Checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.

Answer

Encode all categorical features using label encoding for simplicity.

Answer

Include all categorical features with high cardinality as-is in the model.

Question 7

A data engineering team needs to ingest streaming data from thousands of IoT devices into a data lake on Amazon S3 for near-real-time analytics. The data must be partitioned by device ID and timestamp, and the team must minimize data loss during ingestion failures. Which solution is MOST appropriate?

Accepted Answer

Use Amazon Kinesis Data Firehose to write directly to S3 with dynamic partitioning.. Amazon Kinesis Data Firehose with dynamic partitioning is the most appropriate solution because it natively supports partitioning incoming data by device ID and timestamp before writing to S3, and it provides built-in data buffering and retry logic to minimize data loss during ingestion failures. Unlike a Lambda-based approach, Firehose handles large-scale streaming ingestion without requiring custom code for partitioning or error handling, making it ideal for near-real-time analytics on IoT data.

Answer

Use Amazon Kinesis Data Streams with a Lambda function that writes to S3.

Answer

Use Amazon S3 Transfer Acceleration with direct uploads from devices.

Answer

Use AWS Lambda to receive data via API Gateway and write to S3.

Question 8

A data scientist is training a linear regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training, the model achieves an R² of 0.85 on the training set but only 0.60 on the test set. Which of the following is the MOST likely cause of this discrepancy?

Accepted Answer

The model is overfitting the training data. A high R² on the training set (0.85) paired with a significantly lower R² on the test set (0.60) is a classic symptom of overfitting. The model has learned noise and specific patterns in the training data that do not generalize to unseen data, causing poor test performance. Regularization techniques like Lasso or Ridge, or reducing model complexity, would typically address this issue.

Answer

There is multicollinearity among the features

Answer

The model is underfitting the training data

Answer

There is data leakage between the training and test sets

Question 9

A company is using Amazon SageMaker to train and deploy a fraud detection model. The model is a gradient boosting machine (GBM) trained on a dataset with 10 million rows and 50 features. The training job runs on an ml.m5.2xlarge instance with 8 vCPUs and 32 GB memory. The training completes successfully, and the model is deployed to a real-time endpoint. After deployment, the inference latency is around 200 ms per request, which is acceptable. However, after a week, the company observes that latency increases to over 1 second during peak hours (12:00-13:00 UTC). CloudWatch metrics show CPU utilization on the endpoint instance reaches 95% during these peaks. The endpoint is configured with a single ml.m5.large instance. The company wants to maintain latency under 500 ms during peak hours without incurring unnecessary cost during off-peak hours. Which solution should the company implement?

Accepted Answer

Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1. Configuring auto scaling based on CPU utilization dynamically adds instances during peak hours (when CPU exceeds target) and removes them during off-peak, maintaining latency under 500 ms while minimizing cost. Option A is incorrect because reducing instances to zero and manually launching a new endpoint is not automated, causes downtime, and is impractical. Option B is incorrect because Batch Transform is designed for offline, batch inference, not for real-time requests. Option D is incorrect because replacing the instance with a larger, always-on type (ml.m5.4xlarge) would handle peak load but incur higher cost during off-peak hours, unlike auto scaling which scales in.

Answer

Reduce the number of instances to zero during off-peak hours and manually launch a new endpoint every day at 12:00

Answer

Switch to SageMaker Batch Transform and have the application send requests in batches

Answer

Replace the endpoint instance type with ml.m5.4xlarge to handle peak load

Question 10

A company uses Amazon EMR to run Spark jobs on a cluster with 10 core nodes of type r5.xlarge. The jobs are I/O intensive and read large amounts of data from S3. The team notices high network throughput but low CPU utilization. Which configuration change would improve job performance at the same cost?

Accepted Answer

Replace the core nodes with r5d.xlarge instances that have local SSDs.. R5d instances include local NVMe SSDs. These SSDs can be used for caching intermediate data during Spark jobs, reducing the need to read from and write to S3 over the network. This directly addresses the I/O bottleneck and high network throughput observed, improving job performance. Option A is incorrect because moving to general-purpose m5 instances does not provide local SSDs and may not improve I/O. Option B is incorrect because doubling the number of core nodes would increase cost significantly without necessarily solving the I/O issue. Option D is incorrect because spot instances reduce cost but do not inherently improve I/O performance; they may even add instability.

Answer

Change the instance type to m5.xlarge (general purpose) to balance resources.

Answer

Increase the number of core nodes to 20.

Answer

Use spot instances for the core nodes to save cost and reinvest in more nodes.

Question 11

A data scientist is building a training dataset from data stored in Amazon S3. The data consists of JSON files each containing a 'timestamp' field. The scientist wants to use AWS Glue to catalog the data and enable querying via Amazon Athena. However, Athena queries are returning zero results for time-range filters. What is the most likely cause?

Accepted Answer

The JSON files are not in the correct format for Athena.. Athena supports querying JSON data, but the JSON files must have a schema that Athena can interpret. If the 'timestamp' field is in an unrecognized date/time format or the JSON structure is inconsistent, Athena may fail to parse the data correctly, resulting in zero rows for time-range filters. Option C is correct because the most likely cause is the JSON files not being in a format that Athena can parse properly for timestamp filtering. Option A is wrong because permission issues would cause an access denied error. Option B is wrong because Athena supports nested JSON. Option D is wrong because even if the timestamp is not a partition column, filtering on it should still return results if the data matches the filter condition.

Answer

The AWS Glue crawler does not have permissions to read the S3 bucket.

Answer

Athena cannot query nested JSON objects.

Answer

The 'timestamp' field is not defined as a partition column in the Glue table.

Question 12

Which TWO actions are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)

Accepted Answer

Apply log transformation to reduce the impact of extreme values.. Applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.

Answer

Replace the mean with the median for numerical features.

Answer

Remove all outliers without further investigation.

Answer

Assume outliers are errors and delete them.

Question 13

During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?

Accepted Answer

Apply log transformation.. A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative data.

Answer

Standardize the features using Z-scores.

Answer

Apply a sine transformation.

Answer

Apply Box-Cox transformation with lambda=0.

Question 14

A machine learning engineer is deploying a model on Amazon SageMaker. Which TWO steps are required to create a SageMaker endpoint?

Accepted Answer

Create a SageMaker model. A is correct because creating a SageMaker model is the first required step to define the model artifacts, inference code, and container image that will be used for predictions. Without a model object, SageMaker has no executable artifact to deploy behind the endpoint.

Answer

Submit a training job

Answer

Create a SageMaker pipeline

Answer

Create a SageMaker notebook instance

Question 15

A team is using SageMaker to train a model with hyperparameter tuning. The training jobs are taking too long. The team wants to reduce time without sacrificing model quality. Which approach should they take?

Accepted Answer

Enable early stopping in the hyperparameter tuning job.. Enabling early stopping terminates poorly performing training jobs early, saving time without sacrificing model quality. Option A is incorrect: random search may be faster but does not guarantee quality as it is less efficient than Bayesian optimization. Option C is incorrect: increasing the maximum number of training jobs would increase time. Option D is incorrect: reducing the maximum runtime per training job may prevent convergence, harming model quality.

Answer

Use random search instead of Bayesian optimization.

Answer

Increase the maximum number of training jobs.

Answer

Reduce the maximum runtime per training job.

Question 16

A data scientist is using Amazon SageMaker Autopilot to automatically build a model. The dataset contains a mix of numerical and categorical features. After the experiment completes, Autopilot provides several candidate pipelines. Which pipeline is MOST likely to be ranked highest by Autopilot?

Accepted Answer

The pipeline with the lowest validation loss. Amazon SageMaker Autopilot ranks candidate pipelines by their objective metric on the validation dataset, which is typically the validation loss (e.g., cross-entropy for classification or mean squared error for regression). The pipeline with the lowest validation loss generalizes best to unseen data, making it the highest-ranked candidate. Autopilot uses hold-out validation or cross-validation to compute this metric, ensuring the ranking reflects out-of-sample performance rather than overfitting to the training set.

Answer

The pipeline with the simplest model (e.g., linear classifier)

Answer

The pipeline with the fastest training time

Answer

The pipeline with the lowest training loss

Question 17

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for PostgreSQL and load it into Amazon Redshift. The Glue job runs nightly and takes 6 hours to complete. The Redshift cluster is a single dc2.large node. The team needs to reduce the load time to under 3 hours. The data volume is 200 GB per night. The team is considering using Amazon Redshift Spectrum to query data directly from S3 instead of loading it. However, the data transformation logic is complex and requires multiple joins and aggregations that are currently performed in Glue. Which approach should the team recommend to meet the time requirement?

Accepted Answer

Increase the Redshift cluster to a multi-node cluster with dc2.8xlarge nodes to improve COPY and query performance.. Increasing the Redshift cluster to a multi-node configuration with dc2.8xlarge nodes significantly improves COPY performance by parallelizing data loading across multiple slices. The current single dc2.large node may be the bottleneck for the data loading portion of the Glue job. Faster loading can reduce the total 6-hour runtime to under 3 hours if loading is the dominant factor. Note: the complex transformations remain in AWS Glue, not on Redshift. Option A is incorrect because Redshift Spectrum allows querying external tables without loading but does not accelerate the complex ETL transformations. Option C splits the load but does not address the single-node ingestion bottleneck and adds complexity. Option D improves load speed with Parquet but the cluster's ingestion capacity is still the limiting factor.

Answer

Use Redshift Spectrum to create external tables and run the transformations directly in Redshift, bypassing the Glue job.

Answer

Split the Glue job into multiple parallel jobs that each load a portion of the data into separate Redshift tables, then use UNION ALL views.

Answer

Stage the data in S3 in Parquet format and use a COPY command with the PARQUET option to load data faster.

Question 18

A company is using Amazon SageMaker to train a model and wants to automatically retrain the model every week using new data. Which AWS service should be used to orchestrate the retraining pipeline?

Accepted Answer

AWS Step Functions. AWS Step Functions is the correct choice because it provides a serverless workflow orchestration service that can coordinate multiple AWS services (e.g., SageMaker training jobs, Lambda functions, and data processing) into a state machine. It supports scheduling via Amazon EventBridge (formerly CloudWatch Events) to trigger the pipeline weekly, and it can handle retries, error handling, and parallel execution, making it ideal for automating a retraining pipeline.

Answer

Amazon CloudWatch Events

Answer

AWS Lambda

Answer

AWS Data Pipeline

Question 19

A data scientist is building a text classification model. The dataset contains 10,000 documents, each labeled with one of 5 categories. Which algorithm is most suitable for this task?

Accepted Answer

Naive Bayes. Naive Bayes is highly suitable for text classification because it models the probability of each category given the document's word features using Bayes' theorem with a strong independence assumption. It performs well on high-dimensional sparse data like bag-of-words or TF-IDF representations, and it is particularly effective when the number of documents (10,000) is moderate relative to the vocabulary size, as it requires relatively little training data to estimate parameters.

Answer

Principal Component Analysis (PCA)

Answer

Linear regression

Answer

k-means clustering

Question 20

A data scientist is using SageMaker to train a model. The training data is stored in an S3 bucket in a different AWS account. What is required to allow SageMaker to access the data?

Accepted Answer

Configure the SageMaker execution role with a policy that grants cross-account access to the S3 bucket.. SageMaker uses an IAM execution role to access resources. To allow cross-account access to an S3 bucket, the SageMaker execution role must have an IAM policy that grants s3:GetObject and s3:ListBucket permissions for the bucket, and the S3 bucket policy must also grant cross-account access to that role. This is the standard AWS mechanism for cross-account resource access.

Answer

Set up VPC peering between the two accounts.

Answer

Create a SageMaker notebook instance in the same account as the S3 bucket.

Answer

Launch the training job from a SageMaker notebook in the account containing the S3 bucket.