MLS-C01 Practice Test 27 — 15 Questions

Question 1

A team uses AWS Glue ETL jobs to preprocess data for SageMaker training. The job runs successfully but the output data is empty. What is the most likely cause?

Accepted Answer

The filter transformation condition is too restrictive, removing all rows. A filter transformation in AWS Glue ETL jobs can remove all rows if the condition is too restrictive, resulting in an empty output dataset. This is a common logic error where the filter predicate (e.g., `df.filter("value > 100")`) matches no records, causing the DynamicFrame to be empty after transformation. The job succeeds because no runtime error occurs, but the output is empty.

Answer

There is a data type mismatch between source and target

Answer

The source data is partitioned and only a subset of partitions is read

Answer

The Glue job runs out of memory and fails silently

Question 2

A company is running a real-time inference endpoint on Amazon SageMaker. The endpoint is using an ml.c5.xlarge instance. Over the past month, the CPU utilization has been consistently below 10%, and the latency is well within requirements. The company wants to reduce costs. What should they do?

Accepted Answer

Use a smaller instance type. The CPU utilization is consistently below 10%, indicating significant over-provisioning. Downgrading to a smaller instance type (e.g., ml.c5.large or ml.t3.medium) directly reduces the per-hour cost while still meeting the latency requirements. This is the most straightforward cost optimization when the current instance is underutilized and performance is already satisfactory.

Answer

Set up a scaling policy to scale down to zero

Answer

Switch to a multi-model endpoint

Answer

Use a batch transform job instead

Answer

Move to a serverless inference endpoint

Question 3

A team is training a large NLP model using SageMaker. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 61 GB GPU memory. Which action should the team take to resolve the issue without changing the model architecture?

Accepted Answer

Reduce the batch size in the training script. Reducing the batch size in the training script decreases GPU memory usage per iteration, which can resolve the OutOfMemory error without changing the model architecture. Option A is incorrect because switching to a regression model changes the problem type. Option B is incorrect because increasing the number of epochs does not affect memory per step. Option C is incorrect because Managed Warm Pools are for reducing cold start times, not for memory issues.

Answer

Switch to a regression model

Answer

Increase the number of epochs

Answer

Enable SageMaker Managed Warm Pools

Question 4

A data science team is deploying a machine learning model to production using SageMaker. The model is a PyTorch model that requires custom inference logic including image preprocessing. The team needs to ensure that the endpoint can handle variable batch sizes and has low latency. Which deployment approach should the team use?

Accepted Answer

Use SageMaker Inference Pipelines with a preprocessing container followed by the PyTorch model container.. SageMaker Inference Pipelines allow chaining multiple containers — such as a preprocessing container and a model container — behind a single endpoint, which is ideal for custom preprocessing plus PyTorch inference. This approach supports variable batch sizes and low-latency real-time inference, matching the team's requirements.

Answer

Deploy the model as an AWS Lambda function and use API Gateway.

Answer

Use the SageMaker Python SDK's Predictor class with the model artifact.

Answer

Use a SageMaker multi-model endpoint to host the model with a custom container.

Answer

Use SageMaker Batch Transform for real-time inference.

Question 5

A data engineer needs to continuously ingest streaming data from thousands of IoT devices and store the raw data in Amazon S3 for archival processing. The data volume varies significantly throughout the day, and the solution must be serverless, scalable, and cost-effective. Which AWS service should be used to capture and buffer the streaming data before writing to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service designed to reliably capture, buffer, and automatically load streaming data into Amazon S3 without requiring any custom code or infrastructure management. It handles variable data volumes by scaling automatically and provides built-in buffering (up to 128 MB or 900 seconds) before writing to S3, making it cost-effective for archival storage.

Answer

Amazon Kinesis Data Streams

Answer

AWS Glue

Answer

Amazon Simple Queue Service (SQS)

Question 6

A company is using SageMaker built-in object detection algorithm to detect defects in manufacturing images. The model is trained on 10,000 labeled images and achieves 95% accuracy. However, in production, the model misclassifies many defective items as non-defective (false negatives). The business requires recall > 90% for the defect class. Which action should they take?

Accepted Answer

Adjust the decision threshold of the model to increase recall at the expense of precision. For a binary classifier, the decision threshold determines the trade-off between precision and recall. Lowering the threshold for the defect class causes more items to be classified as defective, which increases recall (fewer false negatives) at the cost of precision. Since the business requires recall > 90% for the defect class, adjusting the threshold is the fastest, lowest-cost action that directly addresses the metric.

Answer

Use a different algorithm such as semantic segmentation

Answer

Use SageMaker's Automatic Model Tuning to find better hyperparameters

Answer

Retrain the model with more images of non-defective items

Question 7

A data scientist wants to deploy a PyTorch model for real-time inference with latency under 100 ms. Which AWS service is most suitable?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically under 100 ms, by hosting a model behind an HTTPS endpoint that auto-scales based on traffic. They support PyTorch natively via pre-built containers or custom containers, making them the most suitable choice for this requirement.

Answer

Amazon SageMaker Processing

Answer

AWS Lambda with container image

Answer

Amazon SageMaker Batch Transform

Question 8

A data scientist is exploring a dataset and wants to understand the distribution of a continuous feature. Which visualization is most appropriate for identifying skewness and potential outliers?

Accepted Answer

Box plot. A box plot summarizes a continuous distribution using the five-number summary (min, Q1, median, Q3, max) and explicitly draws whiskers at 1.5×IQR, so points beyond the whiskers are flagged as potential outliers. The position of the median within the box and the symmetry of the whiskers make skewness visually obvious. That combination is exactly what the question asks for.

Answer

Bar chart

Answer

Scatter plot

Answer

Heatmap

Question 9

A company is using Amazon SageMaker to build a binary classification model. The dataset is highly imbalanced, with 95% negative class and 5% positive class. Which technique should be used to address the class imbalance?

Accepted Answer

Use a weighted loss function during training.. Using a weighted loss function (e.g., class weights in SageMaker's built-in XGBoost or custom PyTorch loss) assigns a higher penalty to misclassifications of the minority positive class. This directly addresses the 95:5 imbalance by making the model more sensitive to the positive class during gradient updates, without discarding data.

Answer

Use accuracy as the primary evaluation metric.

Answer

Perform random under-sampling of the majority class.

Answer

Remove all examples from the majority class.

Question 10

A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited for this task?

Accepted Answer

Amazon SageMaker Processing. Amazon SageMaker Processing allows you to run custom data processing scripts (e.g., using pandas) that can handle grouping rare categories into 'Other' based on frequency thresholds. Option B (Data Wrangler) is a visual tool that may not offer the same level of customization for complex grouping logic. Option C (AWS Glue Studio) is a visual ETL tool but lacks tight integration with SageMaker and may be less efficient for this specific task. Option D (Autopilot) is designed for automated model building, not custom data processing.

Answer

Amazon SageMaker Data Wrangler

Answer

AWS Glue Studio

Answer

Amazon SageMaker Autopilot

Question 11

Which TWO of the following are best practices for hyperparameter tuning using Amazon SageMaker? (Choose 2)

Accepted Answer

Use early stopping to terminate poorly performing training jobs.. Option B is correct because SageMaker automatic model tuning supports early stopping, which stops underperforming training jobs before they run all epochs, saving time and compute cost while still allowing the tuner to focus resources on promising hyperparameter combinations. Option E is correct because SageMaker's automatic model tuning (the CreateHyperParameterTuningJob API) uses Bayesian optimization by default, which intelligently searches the hyperparameter space and typically finds good configurations with fewer training jobs than exhaustive methods. Option A is not a best practice because grid search exhaustively evaluates every combination, which is computationally expensive and inefficient compared with SageMaker's Bayesian or random search strategies. Option C is incorrect because including every algorithm hyperparameter bloats the search space and slows tuning; you should tune only the hyperparameters that materially affect model performance. Option D is incorrect because increasing the training dataset size is a data strategy, not a hyperparameter tuning best practice, and it does not by itself improve the tuning process.

Answer

Use grid search to exhaustively explore all combinations.

Answer

Include all algorithm hyperparameters in the tuning job.

Answer

Use a larger training dataset to improve tuning results.

Question 12

During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following is the most likely cause?

Accepted Answer

Overfitting.. A large gap between training AUC (0.99) and validation AUC (0.72) is the classic signature of overfitting: the model has memorized training data including noise, so it performs excellently on seen data but generalizes poorly. The high training score combined with degraded validation performance is diagnostic. Regularization, more data, or simpler models would typically be the remedy.

Answer

Class imbalance in the training set.

Answer

Underfitting.

Answer

Data leakage from validation to training.

Question 13

An ML engineer is performing EDA on a dataset of customer transactions. The dataset has 1 million rows and 20 columns, including a 'transaction_amount' column. The engineer notices that 5% of the transaction amounts are negative, which are data entry errors. The rest are positive. Which approach is most appropriate for handling these negative values during EDA?

Accepted Answer

Remove rows with negative transaction amounts from the dataset.. Negative transaction amounts are explicitly identified as data entry errors, not legitimate refunds or reversals. During EDA, the cleanest and most defensible treatment is to remove those erroneous rows so they do not distort summary statistics, distributions, or downstream feature engineering. Imputing or transforming them would fabricate data that never existed.

Answer

Impute the negative values with the median of positive transaction amounts.

Answer

Take the absolute value of the negative transaction amounts.

Answer

Cap the negative values at zero.

Question 14

A data scientist is analyzing a dataset stored in Amazon S3 (100 GB, CSV format) using Amazon SageMaker Studio. The dataset contains 500 columns and 10 million rows. The data scientist wants to understand the distribution of each column, detect missing values, and identify outliers. However, the SageMaker Studio notebook instance runs out of memory when loading the entire dataset into a pandas DataFrame. The data scientist needs to complete the EDA efficiently without modifying the source data. What should the data scientist do?

Accepted Answer

Use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data.. SageMaker Data Wrangler is purpose-built for EDA on large datasets; it automatically samples data and profiles columns without requiring the entire dataset to be loaded into memory. Option A (sampling 10% of rows) could work but risks missing critical patterns or outliers, and is less integrated than Data Wrangler. Option B (converting to Parquet with AWS Glue) adds complexity and still requires memory to load into pandas. Option C (larger instance) may still be insufficient and is more expensive. Data Wrangler provides a seamless, integrated experience within SageMaker Studio for efficient EDA.

Answer

Write a script that loads only a random 10% sample of rows to reduce memory usage.

Answer

Use AWS Glue ETL to transform the data into Parquet format and then load into pandas.

Answer

Launch a larger notebook instance with more memory (e.g., ml.r5.24xlarge) and reload the data.

Question 15

A data scientist is trying to upload a CSV file to an S3 bucket using the AWS CLI without specifying server-side encryption. The upload fails with an AccessDenied error. Based on the bucket policy exhibit, what is the most likely cause?

Accepted Answer

The upload request did not specify the required server-side encryption.. When an S3 bucket policy includes a condition requiring server-side encryption (e.g., s3:PutObject with a StringNotEquals on s3:x-amz-server-side-encryption), any upload that omits the encryption header is denied. The AWS CLI's default cp command does not automatically add SSE headers, so the request fails with AccessDenied even though the IAM identity has baseline permissions. The fix is to add --sse AES256 or --sse aws:kms to the CLI command.

Answer

The bucket does not exist.

Answer

The data scientist does not have any permissions to the bucket.

Answer

The data scientist used the wrong AWS region.