MLS-C01 Practice Test 2 — 10 Questions

Question 1

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target variable is binary. After checking for multicollinearity, the scientist finds many pairs of features with correlation > 0.95. Which action should be taken to prepare the data for modeling?

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

Accepted Answer

For each highly correlated pair, remove one feature based on domain knowledge or higher correlation with target.. When features are highly correlated (e.g., > 0.95), they introduce multicollinearity, which can destabilize coefficient estimates in linear models and reduce interpretability. Removing one feature from each correlated pair based on domain knowledge or its correlation with the target variable preserves predictive power while reducing redundancy. This approach is more targeted than PCA, which transforms features into uncorrelated components but sacrifices interpretability and may not align with the binary target.

Answer

Apply PCA to all features to decorrelate them.

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Standardize all features using StandardScaler.

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Randomly drop half of the correlated features.

Question 2

A data scientist is training a model using Amazon SageMaker. The training dataset is 500 GB and is stored in S3. The data scientist wants to use Pipe input mode to stream data directly from S3 to the training container. However, the training job fails with an error indicating that the container cannot read the data. What is the most likely cause?

Accepted Answer

The training algorithm does not support Pipe mode. Pipe mode in SageMaker streams data directly from S3 to the training container via a FIFO pipe, but the training algorithm must be specifically designed to read from a pipe (e.g., via stdin or a named pipe). If the algorithm expects random access to files or uses libraries that require seekable file handles (like many image-processing or custom Python scripts using `open()`), it will fail because a pipe is a sequential, non-seekable stream. SageMaker’s built-in algorithms like BlazingText and XGBoost support Pipe mode, but custom algorithms often do not unless explicitly coded for it.

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The training instance does not have enough memory

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The IAM role does not have s3:GetObject permission

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The data is compressed and Pipe mode cannot handle compressed data

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The S3 bucket is in a different Region

Question 3

A company uses Amazon SageMaker to train a deep learning model for image classification. The training dataset consists of 500,000 images, each 256x256 pixels, stored in S3. The team uses a single ml.p3.2xlarge instance for training. The training time is unacceptably long (over 48 hours). The team wants to reduce training time without sacrificing model accuracy. They have already optimized the data pipeline by using SageMaker Pipe mode and sharding the S3 dataset. The model is a ResNet-50 implemented in TensorFlow. The team is considering the following options: A) Switch to a ml.p3.16xlarge instance which has 8 GPUs and more memory. B) Implement distributed data parallelism using Horovod across multiple instances. C) Use SageMaker's built-in Hyperparameter Tuning to find optimal hyperparameters. D) Reduce the image resolution to 128x128 to speed up training. Which option will MOST effectively reduce training time while maintaining accuracy?

Accepted Answer

Implement distributed data parallelism using Horovod across multiple instances. Using multiple instances with Horovod for distributed data parallelism can scale training linearly with the number of GPUs, significantly reducing time. A larger single instance (ml.p3.16xlarge) provides 8 GPUs but still limited by single instance. Hyperparameter tuning does not directly reduce training time. Reducing resolution may lose accuracy.

Answer

Switch to a ml.p3.16xlarge instance

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Reduce the image resolution to 128x128

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Use SageMaker's built-in Hyperparameter Tuning

Question 4

A company is deploying a fraud detection model using Amazon SageMaker. The model is a linear learner trained on 100 GB of data. For inference, the model receives individual transactions and must return a prediction within 100 ms. Which endpoint configuration should the team use to meet the latency requirement?

Accepted Answer

Deploy a single model endpoint using a GPU instance and enable autoscaling.. A single-model endpoint on a GPU instance provides the low-latency, high-throughput inference required for real-time fraud detection. GPU instances accelerate linear learner inference by parallelizing matrix operations, enabling sub-100 ms predictions for individual transactions. Autoscaling ensures the endpoint can handle traffic spikes without degrading latency.

Answer

Use a multi-model endpoint with CPU instances.

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Use a batch transform job scheduled every minute.

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Deploy using SageMaker Serverless Inference.

Question 5

A data science team is deploying a machine learning model using Amazon SageMaker. The model requires GPU inference and must handle variable traffic with low latency. Which TWO options should the team implement to meet these requirements? (Choose TWO.)

Accepted Answer

Use a SageMaker multi-model endpoint with a GPU instance to serve multiple models.. A is correct because a SageMaker multi-model endpoint with a GPU instance allows you to host multiple models on a single endpoint, dynamically loading and unloading them based on traffic, while providing GPU acceleration for low-latency inference. This approach efficiently handles variable traffic patterns by scaling the endpoint and leveraging GPU compute for deep learning models.

Answer

Deploy to a SageMaker real-time endpoint using a CPU instance and attach an Elastic Inference accelerator.

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Use AWS Lambda with an attached GPU function for inference.

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Host the model on a SageMaker batch transform job with GPU instances.

Question 6

A company uses Amazon EMR to run Spark jobs on a transient cluster that processes data from S3. The jobs are failing with 'OutOfMemory' errors. The data engineer has already increased the executor memory. Which additional configuration change would MOST likely resolve the issue?

Accepted Answer

Increase the number of partitions in the data. The 'OutOfMemory' errors in Spark on EMR typically occur when individual partitions hold too much data for the executor's memory to process. Increasing the number of partitions distributes the data more evenly across available memory, reducing the per-partition size and preventing memory overflow during shuffle or aggregation operations. This directly addresses the root cause of memory pressure, whereas simply increasing executor memory may only delay the failure.

Answer

Use fewer, larger instance types for the core nodes

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Increase the driver memory

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Increase the number of executors

Question 7

A team is building a data pipeline that ingests data from an Amazon S3 bucket, transforms it using AWS Glue, and loads it into Amazon Redshift for analysis. The Glue job runs on a schedule every hour. The team has noticed that the job takes longer than expected and sometimes fails due to memory issues. The data volume is variable, with occasional spikes. Which solution should the team implement to optimize the pipeline?

Accepted Answer

Enable job bookmarks to process only new data and use a G.2X worker type for more memory.. Enabling job bookmarks allows the Glue job to process only new or changed data since the last run, reducing the data volume per execution. Using the G.2X worker type provides additional memory (e.g., 16 GB per DPU vs. 4 GB for G.1X), which helps prevent out-of-memory failures during data spikes. Together, these optimizations address both the variable data volume and memory constraints without requiring a complete pipeline redesign.

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Decrease the number of workers to reduce memory contention.

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Increase the schedule frequency to run the job more often with smaller data increments.

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Replace AWS Glue with Amazon EMR using Spark.

Question 8

A data scientist is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error message indicating that the container exited with a non-zero code. Which THREE steps should the data scientist take to diagnose the issue? (Choose THREE.)

Accepted Answer

Use the SageMaker Debugger to capture system metrics and output tensors for analysis.. SageMaker Debugger can capture system metrics (e.g., CPU/GPU utilization, memory) and output tensors during training, which helps identify issues like resource exhaustion or problematic gradients that cause non-zero exit codes. It provides deep visibility into the training process without requiring code changes, making it a powerful diagnostic tool for custom container failures.

Answer

Retry the training job with the same configuration; the error might be transient.

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Increase the number of training instances to distribute the workload.

Question 9

A data scientist is training a text classification model using Amazon SageMaker. The dataset consists of 100,000 labeled documents. The data scientist notices that the model performs well on the training set but poorly on the validation set. Which regularization technique should the data scientist apply to reduce overfitting?

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on any single feature and forces it to learn more robust representations. This directly addresses the overfitting symptom of high training accuracy and low validation accuracy by reducing the model's capacity to memorize noise in the training data.

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Data augmentation

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Batch normalization

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Early stopping

Question 10

A data scientist runs a logistic regression and obtains a model with 95% accuracy on the training set. However, the model performs poorly on the test set. Which exploratory data analysis step should have been performed to identify this issue?

Accepted Answer

Checking for class imbalance in the target variable. Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A (correlation matrix) is wrong because it helps with multicollinearity, not class imbalance. Option B (log transformation) is wrong because it addresses skewness in features, not class imbalance. Option D (heatmap of missing values) is wrong because it shows missing data patterns, not class imbalance.

Answer

Generating a correlation matrix of features

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Log transformation of skewed features

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Creating a heatmap of missing values