MLS-C01 Mock Exam 93 — 25 Questions

Question 1

A data scientist is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?

Accepted Answer

Use AWS Glue DataBrew to create a profile job that outputs data quality reports. AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option B is wrong because SageMaker Data Wrangler requires more manual setup and coding. Option C is wrong because Athena requires SQL queries and is not a profiling tool. Option D is wrong because Glue ETL jobs require writing code.

Answer

Use Amazon SageMaker Data Wrangler to import the data and generate a report

Answer

Use Amazon Athena to run SELECT statements on each column

Answer

Use AWS Glue ETL jobs with PySpark to compute statistics

Question 2

A company uses Amazon S3 to store historical transaction data in CSV format. The data is partitioned by transaction_date. A data analyst runs Amazon Athena queries that frequently filter on customer_id and transaction_date. The queries are slow and expensive. The team needs to improve query performance and reduce cost. Which combination of actions should the team take? (Choose TWO.)

Accepted Answer

Convert the data from CSV to Parquet format.. Option B and D are correct. Converting to Parquet reduces data scanned due to columnar storage and compression. Partitioning by customer_id (which is frequently filtered) improves partition pruning. Option A is wrong because increasing workers is not applicable to Athena (serverless). Option C is wrong because converting to JSON increases size. Option E is wrong because using S3 Select may not integrate with Athena directly.

Answer

Enable S3 Select pushdown in Athena to reduce data transfer.

Answer

Convert the data to JSON format for better query performance.

Answer

Increase the number of Athena query workers.

Question 3

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?

Accepted Answer

Use SageMaker Data Wrangler to import the data and generate a data quality report.. SageMaker Data Wrangler can profile the data without writing code. Option A is wrong because Glue Crawler creates a schema but not statistics. Option B is wrong because writing a Spark job is overkill. Option D is wrong because QuickSight requires data import.

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 4

A data scientist is performing EDA on a high-dimensional dataset with 500 features. They want to visualize the data in 2D to check for clusters. They first apply PCA and get a 2D projection that shows no clear structure. They suspect that the data lies on a non-linear manifold. Which of the following techniques should they try next?

Accepted Answer

Use t-distributed Stochastic Neighbor Embedding (t-SNE).. Option D is correct because t-SNE is designed for non-linear dimensionality reduction and visualization. Option A is wrong because PCA is linear. Option B is wrong because LDA is supervised. Option C is wrong because ICA separates independent components, not for visualization.

Answer

Use Independent Component Analysis (ICA).

Answer

Use Linear Discriminant Analysis (LDA).

Answer

Apply PCA again with more components.

Question 5

A data analyst is examining a dataset with a target variable that has three classes: A, B, C. They plot the distribution of a feature 'X' for each class and notice that for classes A and B, the distributions are bimodal, while for class C it is unimodal. They want to assess whether feature 'X' is useful for separating the classes. Which of the following metrics should they compute to quantify the separability?

Accepted Answer

ANOVA F-statistic between feature X and the target.. Option A is correct because ANOVA F-statistic tests if means across groups are significantly different. Option B is wrong because chi-square is for categorical features. Option C is wrong because mutual information is for feature selection but doesn't directly test separability. Option D is wrong because variance ratio is not standard.

Answer

Variance ratio (between-group variance / within-group variance).

Answer

Chi-square test of independence.

Answer

Mutual information between X and the target.

Question 6

A data scientist is working on a project to predict customer churn. The dataset contains 50,000 rows and 20 features, including categorical variables like 'Region' (10 categories) and 'SubscriptionType' (5 categories). The target variable is binary (churn or not). During exploratory data analysis, they plot the distribution of each feature and notice that 'Region' has a highly imbalanced distribution: one region accounts for 80% of the data. Which of the following is the most appropriate next step?

Accepted Answer

Group rare categories into an 'Other' category.. Option B is correct because imbalanced categorical features may cause the model to ignore rare categories; grouping rare levels into an 'Other' category can improve model performance. Option A is wrong because removing the feature could discard useful information. Option C is wrong because one-hot encoding does not address imbalance. Option D is wrong because oversampling addresses target imbalance, not feature imbalance.

Answer

Apply one-hot encoding to the 'Region' feature.

Answer

Remove the 'Region' feature from the dataset.

Answer

Oversample the minority classes in the target variable.

Question 7

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.. Option D is correct because hierarchical clustering on correlations groups correlated features; then one can select a representative from each cluster. Option A is wrong because PCA creates new features but does not remove original ones. Option B is wrong because Lasso performs feature selection but may not handle multicollinearity well. Option C is wrong because mutual information does not capture pairwise redundancy directly.

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 8

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.. Option C is correct because removing one of the highly correlated features reduces multicollinearity. Option A is wrong because PCA is not necessary for just two correlated features. Option B is wrong because standard scaling does not address correlation. Option D is wrong because adding interaction terms increases multicollinearity.

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 9

A data analyst is performing EDA on a dataset containing timestamps of user logins. They want to understand daily login patterns. The timestamp column is in Unix epoch format (integer). Which of the following is the most appropriate transformation to extract day-of-week patterns?

Accepted Answer

Convert the timestamps to datetime objects and extract the day-of-week.. Option B is correct because converting to datetime allows extracting day-of-week. Option A is wrong because binning into hours loses day information. Option C is wrong because converting to string does not facilitate analysis. Option D is wrong because scaling does not help.

Answer

Convert the timestamps to string and split into date and time.

Answer

Apply min-max scaling to the timestamp values.

Answer

Bin the timestamps into 1-hour intervals.

Question 10

A data scientist is working with a dataset that contains a 'Price' column. After plotting a histogram, they observe that the distribution is right-skewed with many extreme high values. They plan to use a linear model that assumes normally distributed errors. Which of the following transformations should they apply to the 'Price' column to make it more normally distributed?

Accepted Answer

Apply log transformation (log(Price)).. Option D is correct because log transformation is commonly used for right-skewed data to reduce skewness. Option A is wrong because min-max scaling does not change distribution shape. Option B is wrong because square transformation increases skewness. Option C is wrong because binning loses information.

Answer

Apply square transformation (Price^2).

Answer

Apply min-max scaling to the 'Price' column.

Answer

Bin the 'Price' values into equal-width intervals.

Question 11

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.. Option C is correct because TF-IDF downweights common words. Option A is wrong because bag-of-words does not weight. Option B is wrong because removing stopwords helps but does not adjust for frequency beyond that. Option D is wrong because word2vec focuses on context, not frequency weighting.

Answer

Remove stopwords from the text.

Answer

Use word2vec embeddings.

Answer

Use a bag-of-words representation.

Question 12

A company is using Amazon SageMaker to train a deep learning model for image segmentation. The training job uses a single ml.p3.2xlarge instance and takes 48 hours to complete. The team needs to reduce training time to under 12 hours to meet a deadline. The dataset is 50 GB of images stored in S3. The team currently uses File mode to download the data to the training instance. The model architecture is a convolutional neural network (CNN) with 50 layers. The team has access to multiple instances of the same type. Which approach will most effectively reduce training time?

Accepted Answer

Use SageMaker's distributed data parallelism with multiple instances.. Option C is correct because SageMaker's distributed data parallelism splits the 50 GB dataset across multiple ml.p3.2xlarge instances, allowing each instance to process a subset of the data in parallel. This can reduce training time from 48 hours to under 12 hours, assuming near-linear scaling with the number of instances (e.g., 4 instances for a 4x speedup). The approach directly addresses the need to reduce wall-clock time without altering the model architecture or data loading method.

Answer

Reduce the number of layers in the CNN to speed up training.

Answer

Increase the batch size on the single instance to process more data per iteration.

Answer

Switch to Pipe mode to stream data from S3, reducing data loading time.

Question 13

A company is using Amazon SageMaker to train a XGBoost model for predicting customer churn. The training data is stored in an S3 bucket as CSV files. The data scientist runs a hyperparameter tuning job with 50 training jobs. The tuning job completes, but the best model's accuracy on the holdout set is lower than expected. The data scientist suspects that the hyperparameter ranges are too narrow. Which corrective action is most appropriate?

Accepted Answer

Expand the hyperparameter ranges for key parameters such as 'max_depth', 'learning_rate', and 'subsample'. Option C is correct because the data scientist suspects the hyperparameter ranges are too narrow, which directly limits the model's ability to find an optimal configuration. Expanding ranges for key XGBoost parameters like 'max_depth', 'learning_rate', and 'subsample' allows the tuning job to explore a broader space of model complexities and regularization levels, potentially improving accuracy on the holdout set. This is the most direct fix for the stated problem, as it addresses the root cause rather than increasing job count or changing the search strategy.

Answer

Increase the number of training jobs in the tuning job

Answer

Switch to a different algorithm like Random Forest

Answer

Change the tuning strategy from random search to Bayesian optimization

Question 14

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 15

A company runs a real-time fraud detection model on a SageMaker endpoint. The model is a TensorFlow neural network trained on transactional data. The endpoint uses a single ml.p3.2xlarge instance. Recently, the application’s latency has increased from 50ms to 500ms on average. The CloudWatch metrics show that CPU utilization is at 90%, GPU utilization is at 30%, and memory utilization is at 40%. The number of requests per second has remained stable. The ML team suspects the model is not fully utilizing the GPU. What action should the team take to reduce latency without changing the instance type?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. Optimizing the model for inference using SageMaker Neo can reduce latency by better leveraging GPU. Option A is wrong because increasing instances only helps throughput, not per-request latency. Option B is wrong because SageMaker Batch Transform is for offline inference. Option D is wrong because CPU instance would not improve GPU utilization.

Answer

Switch to SageMaker Batch Transform to process requests in batches

Answer

Change the endpoint to a compute-optimized instance like ml.c5.large

Answer

Increase the number of instances behind the endpoint and use a load balancer

Question 16

A research lab is using SageMaker to train deep learning models on a custom dataset stored in S3. Each training job uses a single ml.p3.2xlarge instance. Recently, training jobs have been failing intermittently with 'NetworkError: Connection reset by peer' during the data download phase. The data scientist notices that the dataset is 50GB and the network throughput is low. The training script uses the default S3 download method (boto3) to copy data from S3 to the local instance storage. Which solution should the data scientist implement to resolve the issue?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3.. Option D is correct because using SageMaker's Pipe mode streams data directly from S3 without writing to local disk, which is more reliable and avoids large downloads. Option A is incorrect because simply retrying may not fix the underlying network issue. Option B is incorrect because using a larger instance does not guarantee improved network reliability. Option C is incorrect because using EBS volumes adds cost and does not solve the network reset issue; data still needs to be downloaded.

Answer

Mount an EBS volume to the instance and copy data there before training.

Answer

Add retry logic in the training script to handle network errors.

Answer

Use a larger instance type like p3.8xlarge for better network bandwidth.

Question 17

A company is using Amazon SageMaker to host a model for real-time inference. The model was trained using SageMaker's built-in Linear Learner algorithm. The endpoint has been running for a week, and the operations team notices that the endpoint's latency has increased from 50 ms to 150 ms over the past few days. The number of requests per second has remained steady at about 200. The team suspects a memory leak in the inference container. What should the team do to diagnose the issue?

Accepted Answer

Enable CloudWatch Logs and use Container Insights to view memory utilization.. Option B is correct because CloudWatch Logs and container insights can show memory usage over time. Option A is wrong because CloudWatch metrics show latency but not memory directly. Option C is wrong because SageMaker Debugger is for training. Option D is wrong because SageMaker Model Monitor is for data drift, not memory leaks.

Answer

Use Amazon CloudWatch to monitor the endpoint's latency metric.

Answer

Use SageMaker Debugger to inspect the inference container.

Answer

Use SageMaker Model Monitor to detect data drift.

Question 18

A media company uses SageMaker to deploy a real-time inference endpoint for content recommendation. The model is a PyTorch model that uses GPU. The endpoint is deployed with an ml.p3.2xlarge instance. Over time, the endpoint's latency increases significantly during peak hours. The company has enabled auto scaling based on CPU utilization. However, the latency spikes occur even when CPU utilization is low. The model is stateless and the inference code is efficient. What is the MOST likely cause of the latency spikes?

Accepted Answer

Auto scaling is configured based on CPU utilization, but the bottleneck is GPU utilization. Option A is correct because GPU utilization is the correct metric for GPU-based models. Auto scaling based on CPU utilization does not capture GPU load. Option B is wrong because there is no mention of memory issues. Option C is wrong because the model is stateless. Option D is wrong because the model is already on GPU.

Answer

The model uses stateful processing that accumulates requests

Answer

The inference container has a memory leak that causes gradual slowdown

Answer

The instance type is too small for the model

Question 19

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job uses a hyperparameter tuning job to optimize hyperparameters. The tuning job has been running for 3 hours and has completed 20 training jobs. The data scientist wants to stop the tuning job early if it is not making progress. What should the data scientist do to accomplish this?

Accepted Answer

Configure the tuning job with early stopping enabled.. Option B is correct because SageMaker's automatic model tuning supports early stopping with training job early stopping type. Option A is wrong because SageMaker Experiments is for tracking, not stopping. Option C is wrong because SageMaker Debugger stops training jobs, not tuning jobs. Option D is wrong because a CloudWatch alarm cannot stop a tuning job directly.

Answer

Set up a CloudWatch alarm to stop the tuning job if a metric does not improve.

Answer

Use SageMaker Experiments to monitor and manually stop the tuning job.

Answer

Use SageMaker Debugger to stop training jobs that are not improving.

Question 20

A retail company runs an e-commerce platform on AWS. They have a Data Engineering team that processes clickstream data using Amazon Kinesis Data Streams (KDS) with a shard count of 5. The data is consumed by an AWS Lambda function that transforms and loads the data into an Amazon S3 bucket partitioned by year/month/day/hour. Recently, the team has noticed that the Lambda function is experiencing throttling errors, and the KDS shard iterator age is increasing, indicating that the consumer cannot keep up with the incoming data rate. The team has already increased the Lambda reserved concurrency to 1000 and enabled batch window of 60 seconds. The metrics show that the Lambda function duration is well under the 5-minute timeout, and there are no errors in the transformation logic. The S3 write operations are not failing. Which course of action would MOST effectively resolve the issue without unnecessary cost or complexity?

Accepted Answer

Increase the number of shards in the Kinesis Data Stream to 20 to increase the parallelism of Lambda consumers.. The core issue is that the Lambda consumer cannot keep up with the incoming data rate, as evidenced by the increasing shard iterator age. Increasing the shard count from 5 to 20 directly increases the number of Kinesis Data Streams shards, which in turn increases the number of concurrent Lambda invocations (one per shard) and the overall throughput of the stream. This addresses the bottleneck at the source without adding unnecessary complexity or cost, as KDS pricing is based on shard hours and Lambda concurrency is already set to 1000.

Answer

Increase the Lambda reserved concurrency to 5000 to allow more parallel executions.

Answer

Increase the batch window to 300 seconds to accumulate more records per invocation and reduce the number of calls.

Answer

Switch to using Amazon Kinesis Data Analytics with a larger instance type to process the stream.