MLS-C01 Practice Test 47 — 15 Questions

Question 1

A machine learning engineer is deploying a model on Amazon SageMaker that was trained using a custom Docker container. The container is stored in Amazon ECR. The engineer creates a SageMaker model and endpoint configuration, but when creating the endpoint, it fails with an error: 'Could not find the inference code at the expected path.' The engineer verified that the container image is correct and the model artifacts are in S3. What is the most likely cause?

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

Accepted Answer

The inference code is not placed in the /opt/ml/model directory inside the container.. The error 'Could not find the inference code at the expected path' indicates that SageMaker cannot locate the inference script (e.g., serve.py, inference.py) inside the container. SageMaker expects the inference code to be placed in the /opt/ml/model/ directory within the container. Option D correctly identifies that the inference code is not placed in /opt/ml/model. Option A is incorrect because container compatibility would cause a different error (e.g., unsupported base image). Option B is incorrect because ECR pull permissions typically result in an 'Unauthorized' error when pulling the image. Option C is incorrect because model artifacts being in the wrong format would cause loading errors, not inference code path errors.

Answer

The container is not compatible with the SageMaker inference environment.

Answer

The SageMaker execution role does not have ECR pull permissions.

Answer

The model artifacts are not in the correct format.

Question 2

A company uses AWS Lambda to process events from Amazon S3. The Lambda function transforms the data and writes results to another S3 bucket. Recently, the function has been failing due to timeout errors when processing large files. Which solution should the data engineer implement?

Accepted Answer

Use S3 Batch Operations with a Lambda function to process objects. S3 Batch Operations is designed to handle large-scale object processing by invoking a Lambda function asynchronously for each object, bypassing the synchronous invocation limits of S3 event notifications. This allows processing of large files without hitting Lambda's 15-minute timeout or memory constraints, as each object is processed independently and the operation can scale to billions of objects.

Answer

Increase the Lambda function memory and timeout limit

Answer

Increase the Lambda timeout to 15 minutes

Answer

Use Amazon SQS to queue the events and process them in batches

Question 3

A data scientist is analyzing a dataset for a binary classification problem. The dataset has 10,000 samples and 200 features. After splitting into training (80%) and test (20%), the data scientist trains a decision tree classifier and achieves 100% accuracy on the training set but only 55% on the test set. Which step should the data scientist take first to address this issue?

Accepted Answer

Prune the decision tree to reduce complexity. The large discrepancy between training and test accuracy indicates overfitting, and pruning the decision tree (e.g., limiting max_depth) reduces overfitting. Option A is wrong because cross-validation is a technique to evaluate model performance but does not directly fix overfitting. Option B is wrong because more data may help but is not the first step; also data is limited. Option C is wrong because more features may worsen overfitting.

Answer

Use cross-validation to evaluate model performance

Answer

Collect more training data

Answer

Add more features to the model

Question 4

A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is using a single GPU instance and is taking too long. The scientist wants to reduce training time without sacrificing model accuracy. The dataset contains 100,000 images of size 256x256. Which change would most effectively reduce training time?

Accepted Answer

Use a smaller image size (e.g., 128x128). Reducing image resolution (e.g., to 128x128) significantly reduces the number of pixels and thus the computational cost per epoch, often with minimal impact on accuracy for many tasks. Using a smaller batch size increases the number of iterations but can actually slow down training. Distributed training with multiple GPUs would reduce time but the question asks for a change that does not sacrifice accuracy; distributed training can sometimes affect convergence but is generally safe. However, reducing resolution is a direct and effective method.

Answer

Reduce the batch size

Answer

Increase the learning rate

Answer

Switch to a distributed training setup with multiple GPUs

Question 5

A data scientist is performing EDA on a dataset with both numeric and categorical features. Which TWO techniques are appropriate for visualizing the relationship between a numeric feature and a binary categorical target?

Accepted Answer

Violin plot grouped by target. Correct options are C (violin plot grouped by target) and D (box plot grouped by target). Both are effective for visualizing the distribution of a numeric feature across two categories of a binary target. A violin plot combines a box plot and a density plot, showing the full distribution shape, while a box plot displays medians, quartiles, and outliers. Option A (histogram) shows distribution of a single numeric variable but does not directly compare groups. Option B (stacked bar chart) is for categorical vs categorical data. Option E (scatter plot) is for two numeric variables.

Answer

Histogram

Answer

Stacked bar chart

Answer

Scatter plot

Question 6

A team needs to automatically retrain a model every week using new data. Which SageMaker feature is designed to schedule and automate this workflow?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines enables building, automating, and scheduling end-to-end ML workflows, making it suitable for weekly retraining. Option A is correct. Option B, SageMaker Automatic Model Tuning, is for hyperparameter optimization, not scheduling. Option C, SageMaker Model Monitor, is for monitoring model quality and drift, not scheduling. Option D, SageMaker Data Wrangler, is for data preparation and feature engineering, not scheduling.

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Model Monitor

Answer

SageMaker Data Wrangler

Question 7

A data scientist needs to profile a large dataset in Amazon S3 to understand its schema, data types, and quality. Which AWS service can automatically generate a data profile with statistics and visualizations?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew provides data profiling capabilities, automatically generating a data profile with statistics and visualizations. Option A (Amazon Athena) is an interactive query service for analyzing data in S3 using standard SQL, but it does not generate data profiles. Option C (Amazon QuickSight) is a business analytics service for creating visualizations and dashboards, but it does not profile data automatically. Option D (Amazon Redshift) is a data warehouse, not a data profiling service.

Answer

Amazon Athena

Answer

Amazon QuickSight

Answer

Amazon Redshift

Question 8

A data scientist is analyzing a dataset with missing values. The missing data is not random and is correlated with other features. Which imputation method is most appropriate to minimize bias?

Accepted Answer

Multiple imputation using MICE. Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because last observation carried forward is only appropriate for time series data where missing values are filled with the previous observation; it does not handle non-random missing data correlated with other features. Option C is wrong because listwise deletion reduces sample size and may introduce bias when data is not missing completely at random. Option D is wrong because mean imputation can bias estimates and reduce variability, especially when missingness is related to other features.

Answer

Last observation carried forward

Answer

Listwise deletion

Answer

Mean imputation

Question 9

A data scientist is evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)

Accepted Answer

One-hot encoding. Options A, B, and D are correct: One-hot encoding (A) can be used for ordinal variables, though it ignores order, it is still valid. Target encoding (B) captures the relationship with the target and respects ordinality. Label encoding (D) preserves the ordinal nature. Option C (hash encoding) is incorrect because it is typically used for high-cardinality nominal variables, not ordinal, and may lose interpretability. Option E (binary encoding) is also incorrect because it is designed for nominal categories and does not maintain order.

Answer

Hash encoding (using feature hashing)

Answer

Binary encoding (convert to binary representation)

Question 10

A data scientist queried an Athena table and got only one row back, but the CSV file is 1 MB. What is the most likely reason?

Accepted Answer

The table is partitioned but the partition is not correctly defined. A 1 MB CSV file likely contains many rows, but querying returns only one row, which indicates that the table's partition mapping is incorrect. Athena uses partitions to minimize data scanned; if the partition definition does not match the actual data location, the query may only read (or miss) certain partitions, resulting in fewer rows. Option B is wrong because a 1 MB file is too large to contain only one row, as typical CSV rows are much smaller. Option C is irrelevant: whether the table is external does not affect row count. Option D is wrong because Athena supports CSV format.

Answer

The CSV file contains only one row

Answer

The table is not an external table

Answer

Athena does not support CSV format

Question 11

A data science team uses Amazon SageMaker to train models on a large dataset stored in S3. The dataset is 500 GB in CSV format and is updated daily. The team wants to optimize data loading for training jobs to reduce I/O wait time. Which data ingestion strategy is MOST effective?

Accepted Answer

Use SageMaker Pipe input mode to stream data directly from S3.. SageMaker Pipe input mode streams data directly from S3 to the training algorithm without writing to the instance's EBS volume, eliminating disk I/O bottlenecks. This is especially effective for large datasets (500 GB) that are updated daily, as it reduces startup time and avoids the need to download the entire dataset before training begins.

Answer

Use SageMaker File input mode and increase the EBS volume size to 1 TB.

Answer

Convert the CSV files to Parquet format and use File input mode.

Answer

Load the data into an Amazon EFS file system and mount it to the training instance.

Question 12

A data scientist is training a deep learning model on Amazon SageMaker using the built-in Object Detection algorithm. The training job is failing with a 'ResourceLimitExceeded' error when trying to launch multiple GPU instances. Which of the following is the MOST likely cause?

Accepted Answer

The account has reached the limit for the number of GPU instances in the current AWS Region.. The 'ResourceLimitExceeded' error indicates that the account has exceeded its service limit for the number of GPU instances (or any SageMaker training instances) in the current AWS Region. Option A is incorrect because a syntax error would result in a different error (e.g., 'ModuleNotFoundError' or 'SyntaxError'). Option B is incorrect because the dataset size does not directly cause a resource limit error; it might cause out-of-memory issues but not a resource limit exceeded. Option D is incorrect because insufficient S3 bucket permissions would cause an 'AccessDenied' error, not 'ResourceLimitExceeded'.

Answer

The training script has a syntax error.

Answer

The dataset is too large for the selected instance type.

Answer

The S3 bucket containing the training data has insufficient permissions.

Question 13

A team is training a neural network for image classification using Amazon SageMaker. The training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which action should the team take?

Accepted Answer

Implement early stopping based on validation loss. Early stopping halts training when the validation loss stops improving (or starts increasing), preventing overfitting. Option A is incorrect because reducing batch size does not directly address overfitting; it may add noise to gradients. Option B is incorrect because adding more convolutional layers increases model complexity, likely worsening overfitting. Option C is incorrect because increasing the learning rate can cause the model to diverge or overshoot minima, not reduce overfitting.

Answer

Reduce the batch size

Answer

Add more convolutional layers

Answer

Increase the learning rate

Question 14

A company wants to build a model to detect fraudulent transactions. The dataset has a highly imbalanced class distribution. Which technique should be used during training to handle class imbalance?

Accepted Answer

Use SageMaker's built-in fraud detection algorithm that applies random under-sampling. Using a fraud detection algorithm that applies random under-sampling is a standard resampling technique to handle class imbalance. AWS SageMaker provides a built-in fraud detection algorithm that incorporates random under-sampling to balance the dataset. Option A is incorrect because adding features does not directly address imbalance. Option C is incorrect as learning rate affects convergence, not imbalance. Option D is incorrect because increasing tree depth alone can lead to overfitting and does not specifically handle class imbalance.

Answer

Add more features to the dataset

Answer

Reduce the learning rate

Answer

Increase the tree depth in XGBoost

Question 15

A machine learning team needs to process a large dataset stored in Amazon S3 using Apache Spark. They want to minimize cost and avoid managing infrastructure. Which AWS service should they use?

Accepted Answer

AWS Glue. AWS Glue is a fully managed, serverless Spark environment that eliminates infrastructure management. It directly meets the requirement to process large datasets in S3 with Apache Spark while minimizing cost, as you only pay for resources consumed during job execution. Glue also integrates natively with S3 and the broader AWS ecosystem, making it the optimal choice for this use case.

Answer

Amazon Athena

Answer

Amazon EMR

Answer

Amazon SageMaker