MLS-C01 Practice Test 3 — 10 Questions

Question 1

A data scientist is tuning a neural network on a small dataset and observes that the training loss decreases but validation loss increases after a few epochs. Which technique should be applied to mitigate this issue?

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

Accepted Answer

Add dropout layers to the model.. The observed behavior—training loss decreasing while validation loss increasing—is a classic sign of overfitting. Adding dropout layers randomly drops a fraction of neurons during training, which forces the network to learn more robust features and reduces co-adaptation, thereby mitigating overfitting. This technique is specifically designed to improve generalization on small datasets.

Answer

Increase the learning rate.

Answer

Remove regularization terms from the loss function.

Answer

Increase the number of epochs.

Question 2

A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is AUC. The training job converges quickly but the final model has low AUC on the validation set. Which hyperparameter should the data scientist adjust to improve validation AUC?

Accepted Answer

Decrease the learning rate and increase the number of rounds. Decreasing the learning rate and increasing the number of rounds is the correct approach because a low learning rate forces the model to take smaller steps toward the optimum, reducing overfitting and allowing more trees to contribute to the ensemble. This combination often improves generalization and validation AUC when the training job converges too quickly, indicating that the model is overfitting or underfitting due to aggressive learning.

Answer

Increase the subsample ratio of training data

Answer

Increase the learning rate

Answer

Increase the maximum depth of trees

Question 3

A data scientist is building a time series forecasting model for monthly sales. The data shows strong seasonality with a yearly pattern. They plan to use Amazon Forecast. Which algorithm should they choose?

Accepted Answer

DeepAR+. DeepAR+ is purpose-built for time series forecasting with strong seasonality, as it uses recurrent neural networks (RNNs) to capture complex temporal dependencies and automatically models seasonal patterns like yearly cycles. Amazon Forecast natively supports DeepAR+ for such use cases, making it the optimal choice over general-purpose or non-forecasting algorithms.

Answer

XGBoost

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K-means clustering

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Linear regression

Question 4

A data scientist is training a neural network for image classification. The dataset has 50,000 images across 100 classes. The model uses a ResNet-50 architecture pre-trained on ImageNet. The training loss decreases rapidly, but validation loss starts to increase after 5 epochs. Which of the following is the most effective technique to address this?

Accepted Answer

Use data augmentation to increase the diversity of the training set. The rapid decrease in training loss followed by an increase in validation loss after only 5 epochs is a classic sign of overfitting. Data augmentation artificially expands the training set by applying random transformations (e.g., rotations, flips, crops) to existing images, which improves the model's generalization and reduces overfitting. This is the most effective technique among the options because it directly addresses the lack of diverse training examples without changing the model architecture or training hyperparameters in a way that could destabilize learning.

Answer

Increase the learning rate

Answer

Add more layers to the network

Answer

Use a smaller batch size

Question 5

Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The training job fails because the input data is not accessible. Which step should the data scientist take to fix the issue?

Accepted Answer

Attach an IAM policy to SageMakerRole that grants s3:GetObject on the bucket. The training job fails because the SageMaker execution role (SageMakerRole) lacks the necessary IAM permissions to read the input data from the S3 bucket. By attaching an IAM policy that grants s3:GetObject on the specific bucket, the role gains the required access. SageMaker uses the execution role to access S3 data, so the role's permissions must explicitly allow the GetObject action.

Answer

Add a VpcConfig to the training job

Answer

Modify the bucket policy to allow s3:GetObject for any principal

Answer

Increase VolumeSizeInGB to 50

Question 6

A financial services company is deploying a machine learning model for credit risk assessment. The model must have an inference latency under 200ms and must be able to handle up to 1000 transactions per second (TPS). The company wants to minimize costs. The model is a gradient boosting model implemented in XGBoost. Which SageMaker deployment option should the team choose?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.. A SageMaker real-time endpoint with multiple instances behind a load balancer provides the sub-200ms inference latency and 1000 TPS throughput required for synchronous, low-latency predictions. XGBoost models are well-suited for real-time endpoints, and horizontal scaling with multiple instances ensures high availability and cost efficiency by matching capacity to demand.

Answer

Use SageMaker Batch Transform to process transactions in batches.

Answer

Use SageMaker asynchronous inference for queued requests.

Answer

Use SageMaker Serverless Inference for automatic scaling.

Question 7

A machine learning team is using Amazon SageMaker to train a model with a custom algorithm packaged in a Docker container. The training job fails with the error 'Error: Unable to locate sagemaker-training toolkit.' What is the MOST likely cause?

Accepted Answer

The container does not include the SageMaker Training Toolkit. The SageMaker Training Toolkit is a required library that provides the interface between SageMaker and custom Docker containers for training jobs. When a custom container does not include this toolkit, SageMaker cannot execute the training script or communicate with the container, resulting in the 'Unable to locate sagemaker-training toolkit' error. This is a mandatory dependency for any custom training container used with SageMaker.

Answer

The container does not have internet access to download dependencies

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The instance type is incompatible with the container

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The training role does not have permissions to access the container repository

Question 8

Which THREE AWS services can be used together to build a serverless data pipeline that ingests streaming data, transforms it, and loads it into Amazon Redshift for analysis?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose (C) is correct because it is a fully managed, serverless service that can ingest streaming data and deliver it directly into Amazon Redshift (via S3 staging and the Redshift COPY command), making it ideal for the load stage of the pipeline. Amazon Kinesis Data Streams (D) is correct because it provides the serverless, scalable ingestion layer for capturing and buffering streaming data before it is consumed or transformed downstream. AWS Lambda (E) is correct because it is a serverless compute service that can be triggered by Kinesis to transform records in flight before they are loaded into Redshift, satisfying the transform requirement without managing servers. Amazon EMR (A) is not correct here because it is a managed big-data cluster service (EC2-based) rather than a serverless component, and it is not needed for this streaming ingest-transform-load pattern. Amazon SQS (B) is not correct because it is a message queue for decoupling applications, not a streaming data ingestion or delivery service for Redshift pipelines.

Answer

Amazon EMR

Answer

Amazon SQS

Question 9

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 10

A data scientist is building a regression model to predict house prices. The dataset contains features like 'number_of_rooms' (integer), 'sqft' (float), 'location' (categorical with 1000 unique values). Which feature engineering approach is BEST for the 'location' feature?

Accepted Answer

Target encoding. Target encoding is the best approach for the 'location' feature because it has 1,000 unique categories, making one-hot encoding infeasible (would create 1,000 dummy columns) and label encoding inappropriate (imposes arbitrary ordinal relationships). Target encoding replaces each category with the mean of the target variable (house price) for that category, capturing the predictive signal of location while keeping the feature as a single numeric column. This balances model performance with dimensionality and avoids overfitting when regularized (e.g., with smoothing or cross-validation).

Answer

Remove the feature

Answer

One-hot encoding

Answer

Label encoding