MLS-C01 Practice Test 21 — 15 Questions

Question 1

A data scientist is training a decision tree classifier and notices that the model performs well on training data but poorly on test data. Which technique should the data scientist use to address this issue?

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

Accepted Answer

Prune the tree. Pruning the tree reduces overfitting by removing branches that have little statistical significance or that capture noise in the training data. This technique improves generalization to unseen test data, which directly addresses the symptom of high training accuracy and low test accuracy.

Answer

Use a different split criterion

Answer

Apply L1 regularization

Answer

Increase tree depth

Question 2

A data scientist is using Amazon SageMaker for hyperparameter tuning. The tuning job uses a Bayesian optimization strategy. After 10 training jobs, the objective metric (validation accuracy) has plateaued at 0.85. The data scientist wants to explore more diverse hyperparameter combinations. What should the data scientist do?

Accepted Answer

Increase the exploration weight in the tuning job configuration.. In Bayesian optimization, the exploration weight controls the trade-off between exploring new hyperparameter regions and exploiting known good regions. Increasing this weight encourages the acquisition function to sample more diverse combinations, which can help escape a plateau. Option C is correct because it directly addresses the need for greater diversity in the search space.

Answer

Decrease the exploration weight in the tuning job configuration.

Answer

Switch to random search strategy.

Answer

Increase the number of parallel training jobs.

Question 3

A training job fails with the error shown. The training script expects a file named 'train.csv' in the 'training' channel. What is the most likely cause?

Accepted Answer

The 'train.csv' file is located inside a subfolder within 's3://my-bucket/data/', and the script expects it directly in the channel path.. The error indicates that the training script cannot find 'train.csv' in the expected location. When SageMaker copies data from an S3 channel path (e.g., 's3://my-bucket/data/') to the training instance, it places the contents of that S3 prefix directly into the channel directory (e.g., '/opt/ml/input/data/training/'). If the CSV file is inside a subfolder (e.g., 's3://my-bucket/data/subfolder/train.csv'), the script will not find it at the top level of the channel path, causing a 'file not found' error.

Answer

The S3 bucket policy denies access to the 'train.csv' file.

Answer

The channel name in the input data configuration does not match the script's expected channel name.

Answer

The training script has a bug that prevents it from reading the file.

Question 4

A data scientist is using Amazon SageMaker to train a large language model with PyTorch. The training job is taking too long. The dataset is stored in S3 and the training script uses the SageMaker PyTorch container. Which change is MOST likely to reduce training time?

Accepted Answer

Use Pipe mode to stream data from S3 instead of downloading.. SageMaker Pipe mode streams data directly from S3 to the training algorithm via a Unix FIFO (named pipe), eliminating the need to first download the entire dataset to the training instance's local storage. This reduces I/O wait time and disk usage, which is especially beneficial for large language models where dataset sizes can be in terabytes, thereby significantly cutting total training time.

Answer

Increase the number of instances in the training job.

Answer

Change the optimizer to AdamW.

Answer

Switch to spot instances to reduce cost.

Question 5

A data engineer needs to build a pipeline that ingests CSV files from an S3 bucket, validates the schema, and loads the data into an Amazon Redshift cluster. The pipeline must handle schema evolution gracefully by adding new columns as they appear in the source files. Which combination of AWS services and configurations would meet these requirements with minimal operational overhead?

Accepted Answer

Use AWS Glue to create a crawler and an ETL job that writes to Redshift, with 'resolveChoice' to handle new columns. AWS Glue provides a fully managed ETL service that can automatically detect schema changes via crawlers and handle new columns in CSV files using the 'resolveChoice' transformation. The Glue ETL job can write directly to Amazon Redshift with minimal operational overhead, as it manages schema evolution without requiring manual intervention or additional infrastructure.

Answer

Use AWS Glue to create a crawler that updates the schema, then use Redshift Spectrum to query the data directly from S3

Answer

Use Amazon Kinesis Data Firehose to ingest the files and load into Redshift, with a Lambda function to detect schema changes

Answer

Use Amazon Athena to create external tables with schema-on-read, and insert results into Redshift using INSERT INTO

Question 6

A data scientist is training a neural network for image classification. The training loss decreases but validation loss increases after a few epochs. Which TWO actions should be taken to address this?

Accepted Answer

Implement early stopping based on validation loss.. Early stopping monitors validation loss and halts training when it stops improving, preventing overfitting. This directly addresses the symptom of decreasing training loss with increasing validation loss, which is a classic sign of overfitting.

Answer

Increase the learning rate.

Answer

Increase the number of epochs.

Answer

Add more convolutional layers.

Question 7

A machine learning team is using Amazon SageMaker to train a large language model. The training script uses PyTorch and the model requires significant memory. The team wants to use model parallelism across multiple GPUs. Which SageMaker feature should they use?

Accepted Answer

SageMaker model parallelism library. SageMaker's model parallelism library (SMP) is designed for distributed training of large models across GPUs. Horovod is for data parallelism, not model parallelism. SageMaker Debugger is for monitoring training. Distributed Training is a generic term; the specific library is SMP.

Answer

SageMaker Distributed Training

Answer

SageMaker Horovod

Answer

SageMaker Debugger

Question 8

A company uses Amazon Redshift for its data warehouse. The data engineering team needs to load 10 TB of data from Amazon S3 into Redshift every night. The team wants to minimize the load time and use the fewest number of COPY commands. The data is in CSV format and is partitioned by date in S3. Which approach should the team take?

Accepted Answer

Use a manifest file with a single COPY command.. Using a manifest file with a single COPY command is the most efficient approach because it allows Redshift to load data from multiple S3 objects (partitioned by date) in parallel, automatically splitting the workload across cluster nodes. This minimizes load time by leveraging Redshift's parallel processing without requiring multiple COPY commands or manual concatenation, and it avoids the overhead of additional services like AWS Glue for a straightforward bulk load.

Answer

Use multiple COPY commands, one per partition.

Answer

Concatenate all data into a single large file before loading.

Answer

Use AWS Glue to transform the data and then load into Redshift.

Question 9

A company deploys a SageMaker model for inference. After a few days, response times increase significantly. CloudWatch metrics show high CPU utilization and memory usage. The model is a large ensemble. What is the most cost-effective solution?

Accepted Answer

Configure SageMaker automatic scaling based on CPU utilization. SageMaker automatic scaling based on CPU utilization is the most cost-effective solution because it dynamically adjusts the number of inference instances in response to real-time demand, adding capacity only when CPU usage is high and removing it when demand drops. This avoids over-provisioning while maintaining performance for the large ensemble model, which is compute-intensive. Other options either introduce manual overhead, are unsuitable for large models, or incur unnecessary cost by permanently using larger instances.

Answer

Use CloudWatch alarms to notify the team, who manually launch additional endpoints

Answer

Migrate the model to AWS Lambda with provisioned concurrency

Answer

Replace the current instance type with a larger one

Question 10

A data scientist submits a SageMaker training job with the provided configuration. The job fails immediately with the error 'Algorithm not found: 382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.2-1'. What is the most likely cause?

Accepted Answer

The ECR repository URI is incorrect or the image does not exist.. The error 'Algorithm not found' indicates that the ECR repository URI specified in the training job configuration does not point to an existing image. The URI '382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.2-1' is the standard SageMaker built-in XGBoost image for the us-west-2 region, but the version '1.2-1' may not exist or the URI is malformed. SageMaker training jobs require a valid ECR image URI to launch the container; if the image is not found in the registry, the job fails immediately with this error.

Answer

The training region is different from the image region.

Answer

The input data format is incorrect.

Answer

The IAM role does not have permission to pull the image.

Question 11

A media company uses SageMaker to train a neural network for content recommendation. The model uses embeddings for users and items. Training is slow and they want to reduce time. The dataset has 10 million users and 1 million items. They have a cluster of 8 p3.16xlarge instances. Which strategy is most likely to reduce training time?

Accepted Answer

Use SageMaker's model parallelism to split the embedding layers across GPUs. SageMaker's model parallelism splits the embedding layers across GPUs, which is essential when the embedding table is too large to fit into the memory of a single GPU. With 10 million users and 1 million items, even with a modest embedding dimension of 256, the embedding layer alone can exceed 10 GB, causing memory bottlenecks that slow training. Model parallelism distributes these large parameters across multiple GPUs, reducing per-GPU memory pressure and enabling larger batch sizes, which directly reduces training time.

Answer

Use data parallelism to replicate the model on each GPU and synchronize gradients

Answer

Reduce the embedding dimension from 256 to 64

Answer

Use a smaller batch size to fit on each GPU

Question 12

An ML team is deploying a model for real-time inference. They require A/B testing to compare a new model against the existing one. Which THREE steps should they take to set up this test?

Accepted Answer

Set up a second production variant for the existing model. To perform A/B testing with SageMaker, you must create a second production variant for the existing model so that both the old and new models are deployed as separate variants under the same endpoint. This allows the endpoint to serve both models simultaneously and compare their performance.

Answer

Set up a SageMaker Batch Transform job for each model

Answer

Configure CloudWatch alarms to trigger variant switching

Question 13

A data science team is deploying a machine learning model to production using Amazon SageMaker. The model requires real-time inference with low latency. Which SageMaker feature should they use to deploy the model?

Accepted Answer

SageMaker Realtime Endpoint. SageMaker Realtime Endpoints are designed for low-latency, synchronous inference, making them the correct choice for serving predictions in real time. They keep the model loaded and ready to respond to individual requests, which is essential for applications requiring immediate responses.

Answer

SageMaker Notebook Instance

Answer

SageMaker Batch Transform

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SageMaker Autopilot

Question 14

A data scientist is training a binary classification model on a dataset with 10,000 features. The model overfits severely. Which technique is MOST appropriate to reduce overfitting?

Accepted Answer

Apply L1 regularization (Lasso). With 10,000 features, L1 regularization (Lasso) is the most appropriate technique because it performs feature selection by shrinking less important feature coefficients to exactly zero. This directly addresses the high-dimensional overfitting by reducing model complexity and removing noise features, which is more effective than other methods for such a large feature space.

Answer

Use early stopping during training

Answer

Use PCA to reduce dimensionality

Answer

Increase the max depth of the model

Question 15

A machine learning engineer is deploying a TensorFlow model to an Amazon SageMaker endpoint. The endpoint is behind an Application Load Balancer (ALB) for A/B testing. The engineer notices that the new variant is not receiving any traffic. What is the most likely cause?

Accepted Answer

The ALB target group weight for the new variant is set to 0.. If the ALB target group weight for the new variant is set to 0, the ALB will not route any traffic to that target group, even if the endpoint is healthy. In A/B testing setups, weights control the proportion of traffic sent to each variant; a weight of 0 effectively disables the variant.

Answer

The new variant's health checks are failing.

Answer

The model is not compatible with the ALB's protocol.

Answer

The ALB is not configured to route to SageMaker endpoints.