MLS-C01 Practice Test 48 — 15 Questions

Question 1

Match each AWS service to its primary purpose in a machine learning pipeline.

Accepted Answer

Amazon SageMaker: Fully managed service for building, training, and deploying machine learning models.. The correct matches associate each service with its primary ML pipeline role. SageMaker is for end-to-end ML, Lambda for serverless inference, S3 for data/model storage, Glue for data preparation. Distractors confuse these roles.

Answer

Amazon SageMaker: Serverless compute service for inference.

Answer

AWS Lambda: Scalable object storage for models.

Question 2

A data scientist is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). The dataset has 10 million rows. The goal is to train a binary classifier. Which technique should be applied during exploratory data analysis to best address the imbalance?

Accepted Answer

Random undersampling of the majority class. Random undersampling of the majority class is a practical approach for large datasets like 10M rows to reduce class imbalance during EDA. Option A (assign higher class weights) is a modeling technique applied during training, not during EDA. Option C (SMOTE) generates synthetic samples but can be computationally expensive for 10M rows. Option D (collect more data) does not guarantee a balanced distribution and may not be feasible.

Answer

Assign higher class weights to the minority class

Answer

Synthetic Minority Oversampling Technique (SMOTE)

Answer

Collect more data for the minority class

Question 3

A data scientist uses SageMaker to train a model. The training job takes 10 hours, but the team needs to reduce costs. Which approach is MOST cost-effective?

Accepted Answer

Enable Managed Spot Training. Managed Spot Training leverages Amazon EC2 Spot Instances, which offer spare compute capacity at up to 90% discount compared to On-Demand instances. This makes it the most cost-effective approach for reducing training costs. Option A is correct. Option B (using Automatic Model Tuning) is designed for hyperparameter optimization, not cost reduction, and may increase cost due to additional training jobs. Option C (using a larger instance type) finishes faster but at a higher per-hour cost, potentially increasing total cost. Option D (using Distributed Training with more instances) increases resource usage and cost, though it may reduce training time.

Answer

Use SageMaker Automatic Model Tuning

Answer

Use a larger instance type to finish faster

Answer

Use SageMaker Distributed Training with more instances

Question 4

A data scientist trains a model using SageMaker and notices that the training loss decreases but validation loss increases after a few epochs. What is the MOST likely issue?

Accepted Answer

The model is overfitting the training data.. Overfitting occurs when the model performs well on training data but poorly on validation data. Option D is correct. Option A is incorrect because a low learning rate would slow convergence, not cause validation loss to increase while training loss decreases. Option B is incorrect because data leakage would cause both training and validation loss to be artificially low. Option C is incorrect because underfitting would show high training loss.

Answer

The learning rate is too low.

Answer

There is data leakage from validation to training.

Answer

The model is underfitting the training data.

Question 5

A company is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The data scientist wants to use the Pipe mode for training to stream data directly from S3 instead of downloading it first. Which of the following is a prerequisite for using Pipe mode?

Accepted Answer

The training data must be in RecordIO-protobuf or TFRecord format.. Pipe mode in Amazon SageMaker streams data directly from S3 to the training algorithm without downloading it first. This mode requires the data to be in a format that supports random access and chunked reading, such as RecordIO-protobuf or TFRecord. Option D is correct because these formats enable efficient streaming. Option A is incorrect: SageMaker can uncompress data on the fly if needed, and compression is not a prerequisite. Option B is incorrect: the S3 bucket does not need public read access; SageMaker uses IAM roles to access the data. Option C is incorrect: Pipe mode works best with multiple sharded files, not a single large file, to allow parallel streaming.

Answer

The training data must be compressed using Gzip.

Answer

The S3 bucket must have public read access.

Answer

The training data must be stored as a single large file.

Question 6

A data scientist is training a deep learning model on SageMaker using a custom Docker container. The training job fails with an error indicating that the container exited with a non-zero status. The CloudWatch logs show 'FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/input/data/training/data.csv''. What is the most likely cause?

Accepted Answer

The S3 data source path is incorrect or the data has not been uploaded.. The error indicates that the training data file 'data.csv' is missing at the expected container path '/opt/ml/input/data/training/'. This typically occurs when the S3 data source path specified in the training job configuration is incorrect or the data has not been uploaded to that S3 location. Option A is wrong because the error is about missing data, not the container entry point. Option C is wrong because the error does not relate to training script syntax. Option D is wrong because the error does not mention model artifacts.

Answer

The container's Docker entry point is misconfigured.

Answer

The training script has a syntax error.

Answer

The model output path in the training job configuration is wrong.

Question 7

A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the model has a high bias. What is the most likely cause?

Accepted Answer

The model is too simple and underfits the data. High bias indicates that the model is underfitting the training data, meaning it is too simple to capture underlying patterns. Option D correctly identifies this cause. Option A is incorrect because too many features typically lead to high variance (overfitting), not high bias. Option B is incorrect because overfitting is associated with high variance, not high bias. Option C is incorrect because while an excessively high regularization parameter can increase bias, it is less likely than the model being too simple; regularization is designed to prevent overfitting, and its improper tuning is not the most common cause of high bias.

Answer

The training dataset has too many features

Answer

The model is too complex and overfits the data

Answer

The regularization parameter is too high

Question 8

A data scientist is training a deep learning model on a GPU instance. The training loss is decreasing, but the validation loss starts increasing after a few epochs. Which action should the data scientist take to address this?

Accepted Answer

Implement early stopping. Early stopping monitors validation loss and stops training when it starts to increase, which directly addresses overfitting. Option A (reduce batch size) is not the best action; while it can affect training dynamics, it does not directly prevent validation loss increase due to overfitting. Option C (increase learning rate) is incorrect as it may cause the model to diverge or overshoot optimal minima. Option D (add more layers) is incorrect because adding layers increases model complexity, which typically worsens overfitting.

Answer

Reduce the batch size

Answer

Increase the learning rate

Answer

Add more layers to the model

Question 9

A machine learning team is deploying a real-time inference endpoint for a recommendation model using Amazon SageMaker. The model takes a long time to load (several minutes) due to its size (5 GB). Which deployment strategy minimizes the cold start latency?

Accepted Answer

Use Multi-Model Endpoints to keep the model loaded between invocations. Multi-Model Endpoints (MME) allow multiple models to be loaded on the same endpoint and keep them cached in memory between invocations, reducing cold start latency for subsequent calls. This is ideal for large models like the 5 GB recommendation model. Option A (single instance with large memory) does not address load time. Option C (Serverless Inference) incurs cold starts on each invocation. Option D (larger instance type with more vCPUs) may speed up loading but does not prevent cold starts after idle periods. Thus, MMEs minimize cold start by maintaining model persistence.

Answer

Use a single instance with a large memory size

Answer

Use SageMaker Serverless Inference

Answer

Use a larger instance type with more vCPUs

Question 10

A company is building a data pipeline to process streaming data from IoT devices. The data must be ingested with low latency, transformed in real-time using custom logic, and stored in Amazon S3 partitioned by device ID and timestamp. Which combination of AWS services should the company use to meet these requirements?

Accepted Answer

Amazon Kinesis Data Streams with AWS Lambda and Amazon S3. Amazon Kinesis Data Streams provides low-latency ingestion of streaming data, AWS Lambda can apply custom transformation logic in real-time, and the transformed data can be stored in Amazon S3 with partitioning by device ID and timestamp using AWS Lambda to write to S3 with appropriate prefix. Option A is incorrect because Kinesis Data Firehose does not support custom transformation without invoking a Lambda function and cannot partition on write at the level of granularity required (device ID and timestamp). Option B is incorrect because Amazon MSK adds operational overhead and is more complex than needed; although an S3 sink connector can write to S3, it does not easily support custom transformation and partitioning by device ID and timestamp without additional configuration. Option C is incorrect because DynamoDB Streams is designed for change data capture from DynamoDB tables and is not suitable for direct ingestion of high-volume IoT streaming data.

Answer

Amazon Kinesis Data Firehose with direct S3 delivery

Answer

Amazon Managed Streaming for Apache Kafka (MSK) with Amazon S3 sink connector

Answer

Amazon DynamoDB Streams with AWS Lambda and Amazon S3

Question 11

A data scientist is using Amazon SageMaker to train a deep learning model for natural language processing. The training job is taking too long to converge. The data scientist wants to speed up training without significantly sacrificing model accuracy. Which THREE strategies should the data scientist consider? (Choose three.)

Accepted Answer

Reduce the model size by using fewer layers or smaller hidden dimensions.. Options A, D, and E are correct. Reducing the model size (A) decreases computational requirements and speeds up training. Mixed precision training (D) uses FP16 to reduce memory usage and accelerate matrix operations on GPUs. Distributed data parallelism (E) allows training across multiple instances, significantly reducing training time. Option B (increasing learning rate by a factor of 10) is likely too aggressive and can cause divergence. Option C (increasing batch size to maximum) may slow convergence due to reduced gradient noise and can cause memory issues.

Answer

Increase the learning rate by a factor of 10 to accelerate convergence.

Answer

Increase the batch size to its maximum possible value to utilize GPU memory fully.

Question 12

A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 95% accuracy but only 10% recall on the positive class. Which metric should be used to evaluate model performance?

Accepted Answer

F1 score. With imbalanced data (95% negative, 5% positive), accuracy is high despite poor positive class performance. The F1 score (harmonic mean of precision and recall) is a better metric because it captures both false positives and false negatives. Here, recall is only 10%, so even if precision is high, F1 score will be low, reflecting poor model quality.

Answer

Accuracy

Answer

Recall

Answer

Precision

Question 13

A data scientist is performing feature engineering for a machine learning model. The dataset contains categorical features with high cardinality. Which THREE techniques are appropriate for encoding high-cardinality categorical features?

Accepted Answer

Target encoding. Target encoding (A) replaces each category with the mean of the target variable, which captures predictive signal and is efficient for high cardinality. Count encoding (D) uses the frequency of each category, providing a simple numeric representation. One-hot encoding with pruning of rare categories (E) reduces dimensionality by creating dummy variables only for frequent categories, avoiding the curse of dimensionality. Binary encoding (B) is not among the most recommended techniques; it converts categories to binary numbers but can be less intuitive and may not handle rare categories well. Label encoding (C) assigns integers arbitrarily, implying ordinal relationships that may mislead the model, making it unsuitable for nominal high-cardinality features.

Answer

Binary encoding

Answer

Label encoding

Question 14

A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has 500 features and 50,000 observations. The model converges but has high bias. Which technique should the data scientist use to reduce bias?

Accepted Answer

Add polynomial features or interaction terms to the feature set.. Adding interaction features or polynomial features allows the linear model to capture non-linear relationships, reducing bias. Option A (L2 regularization/ridge) penalizes large coefficients, which primarily reduces variance, not bias. Option C (decreasing the learning rate) affects the step size during gradient descent, influencing convergence speed and stability, but does not directly reduce bias. Option D (feature selection) removes irrelevant features, which can reduce overfitting (variance) and may increase bias if important features are removed. Option E (increasing the number of training epochs) gives the model more iterations to converge, but if the model already converges, more epochs won't reduce bias; it might reduce underfitting if the model hasn't fully converged, but typically bias is addressed by model complexity.

Answer

Apply L2 regularization (Ridge) to penalize large coefficients.

Answer

Decrease the learning rate.

Answer

Use feature selection to remove irrelevant features.

Answer

Increase the number of training epochs.

Question 15

A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million samples with 200 features. The data is highly imbalanced (0.1% fraud). The team wants to use a random forest model. Which technique should they use to handle the class imbalance during training?

Accepted Answer

Synthetic Minority Over-sampling Technique (SMOTE). SMOTE generates synthetic samples of the minority class, effectively balancing the dataset before training. This is particularly useful for random forest as it learns from the augmented data directly. Option B (class weights) adjusts the loss function but may not work well with random forest's tree-based structure, and it's not a standard technique for this algorithm. Option C (undersampling) discards majority class data, potentially losing valuable information. Option D (threshold adjustment) is a post-training step and does not address imbalance during the training phase.

Answer

Use class weights inversely proportional to class frequencies

Answer

Random undersampling of the majority class

Answer

Adjust the decision threshold after training