A company is developing an ML model to forecast future values based on time series data. The dataset includes historical measurements collected at regular intervals and categorical features. The model needs to predict future values based on past patterns and trends.
Which algorithm and hyperparameters should the company use to develop the model?
A. Use the Amazon SageMaker AI XGBoost algorithm. Set the scale_pos_weight hyperparameter to adjust for class imbalance.
B. Use k-means clustering with k to specify the number of clusters.
C. Use the Amazon SageMaker AI DeepAR algorithm with matching context_length and prediction_length hyperparameters.
D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm with contamination to set the expected proportion of anomalies.
Show Answer
Correct Answer: C
Explanation: DeepAR is purpose-built for probabilistic time series forecasting using historical observations and can incorporate categorical features. The context_length controls how much historical data the model uses as input, and prediction_length specifies the forecast horizon. XGBoost with scale_pos_weight is for imbalanced classification, k-means is for clustering, and Random Cut Forest is for anomaly detection.
Question 12
Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?
A. Use SageMaker Experiments to facilitate the approval process during model registration.
B. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
C. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
Show Answer
Correct Answer: D
Explanation: SageMaker Pipelines can orchestrate model registration, while the SageMaker Model Registry tracks each model version’s approval status. A designated approver can review a version and use the AWS SDK to change its status to Approved; production deployment can then be limited to approved versions. Experiments, lineage tracking, and Model Monitor do not manage model approval.
Question 13
A company is developing ML models by using PyTorch and TensorFlow estimators with Amazon SageMaker AI. An ML engineer configures the SageMaker AI estimator and now needs to initiate a training job that uses a training dataset.
Which SageMaker AI SDK method can initiate the training job?
A. fit method
B. create_model method
C. deploy method
D. predict method
Show Answer
Correct Answer: A
Explanation: In the SageMaker Python SDK, the estimator.fit() method starts a training job using the specified training data. create_model() creates a model object from trained artifacts, deploy() deploys a model to an endpoint, and predict() performs inference using a predictor.
Question 13
Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?
A. Use Managed Spot Training.
B. Use SageMaker managed warm pools.
C. Use SageMaker Training Compiler.
D. Use the SageMaker distributed data parallelism (SMDDP) library.
Show Answer
Correct Answer: B
Explanation: SageMaker managed warm pools retain and reuse provisioned training infrastructure between consecutive jobs, reducing the time spent starting up new instances. Managed Spot Training primarily reduces costs, while Training Compiler and SMDDP optimize training execution rather than infrastructure startup.
Question 14
A company is developing an application that reads animal descriptions from user prompts and generates images based on the information from the prompts. The application reads a message from an Amazon Simple Queue Service (Amazon SQS) queue. Then the application uses Amazon Titan Image Generator on Amazon Bedrock to generate an image based on the information in the message. Finally, the application removes the message from SQS queue.
Which IAM permissions should the company assign to the application’s IAM role? (Choose two.)
A. Allow the bedrock:InvokeModel action for the Amazon Titan Image Generator resource.
B. Allow the bedrock:Get* action for the Amazon Titan Image Generator resource.
C. Allow the sqs:ReceiveMessage action and the sqs:DeleteMessage action for the SQS queue resource.
D. Allow the sqs:GetQueueAttributes action and the sqs:DeleteMessage action for the SQS queue resource.
E. Allow the sagemaker:PutRecord* action for the Amazon Titan Image Generator resource.
Show Answer
Correct Answer: A, C
Explanation: The application must invoke the Amazon Titan Image Generator model through Amazon Bedrock, which requires the bedrock:InvokeModel permission. It also needs to receive messages from the Amazon SQS queue and delete them after successful processing, requiring sqs:ReceiveMessage and sqs:DeleteMessage permissions. The other options either grant unnecessary read-only Bedrock permissions, insufficient SQS permissions, or unrelated SageMaker permissions.
Question 14
Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?
A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
C. Use the SageMaker Model Registry and model groups to catalog the models.
D. Use the SageMaker Model Registry and unique tags for each model version.
Show Answer
Correct Answer: C
Explanation: Use SageMaker Model Registry model groups to catalog related models. Each model package added to a group is automatically assigned a sequential version, providing centralized model versioning with minimal operational overhead. ECR stores container images, and unique tags are not a substitute for the Model Registry’s built-in versioning.
Question 15
A company develops a recommendation model and hosts the model on an Amazon SageMaker AI endpoint. The model uses the SageMaker AI endpoint to perform near real-time inference to deliver personalized product recommendations to customers based on browsing history, purchase records, and in-app user interactions.
After a major marketing campaign, the company observes a sharp drop in the model’s performance. The company needs a solution to proactively monitor, detect, and validate model performance before future marketing campaigns.
Which solution will meet these requirements?
A. Use SageMaker Clarify to analyze changes in feature distribution. Configure SageMaker Model Monitor for near real-time input validation.
B. Use Amazon CloudWatch dashboards to monitor endpoint metrics. Use SageMaker Model Monitor to track feature attribution.
C. Use SageMaker Clarify for bias detection. Set up Amazon CloudWatch alarms to monitor model latency.
D. Use SageMaker Model Monitor to monitor constraints. Use Amazon CloudWatch Logs Insights to analyze error patterns.
Show Answer
Correct Answer: A
Explanation: The issue described is likely model performance degradation caused by data drift after a marketing campaign. SageMaker Clarify can analyze feature distribution changes and explainability-related characteristics, while SageMaker Model Monitor can perform near real-time data quality monitoring and input validation against baselines to detect drift before it impacts model performance. The other options focus primarily on infrastructure metrics, bias, latency, or log analysis rather than proactive validation of model inputs and data drift.
Sources:
https://docs.aws.amazon.com/sagemaker/latest/dg/inference-recommender.html
Question 16
A company is creating an ML model to identify defects in a product. The company has gathered a dataset and has stored the dataset in TIFF format in Amazon S3. The dataset contains 200 images in which the most common defects are visible. The dataset also contains 1,800 images in which there is no defect visible.
An ML engineer trains the model and notices poor performance in some classes. The ML engineer identifies a class imbalance problem in the dataset.
What should the ML engineer do to solves this problem?
A. Use a few hundred images and Amazon Rekognition Custom Labels to train a new model.
B. Undersample the 200 images in which the most common defects are visible.
C. Oversample the 200 images in which the most common defects are visible.
D. Use all 2,000 images and Amazon Rekognition Custom Labels to train a new model.
Show Answer
Correct Answer: C
Explanation: The dataset is highly imbalanced, with only 200 defect images versus 1,800 non-defect images. A standard way to address class imbalance is to oversample the minority class so the model sees defect examples more frequently during training. Undersampling the defect class would worsen the imbalance, and using Amazon Rekognition Custom Labels does not by itself solve the imbalance problem.
Question 17
An ML engineer is building an ML model in Amazon SageMaker AI. The ML engineer needs to load historical data directly from Amazon S3, Amazon Athena, and Snowflake into SageMaker AI.
Which solution will meet this requirement?
A. Use AWS Glue DataBrew to import the data into SageMaker AI.
B. Build a pipeline in SageMaker Pipelines to process the data. Use AWS DataSync to load the processed data into SageMaker AI.
C. Create a feature store in SageMaker Feature Store. Use an Apache Spark connector to Feature Store to access the data.
D. Use SageMaker Data Wrangler to query and import the data.
Show Answer
Correct Answer: D
Explanation: SageMaker Data Wrangler provides built-in connectors to import and prepare data directly from Amazon S3, Amazon Athena, and Snowflake for use in SageMaker. The other options do not directly satisfy the requirement of loading data from all of these sources into SageMaker for model development.
Question 18
A company is using Amazon SageMaker AI to create a classification model to categorize the company’s sales performance for each month of the previous 20 years on a scale from 1 to 5. The dataset includes fields for month, sales region, regional aggregate sales, and the number of stores in each sales region. The company notices that during two months of every year, the aggregate sales values are unexpectedly high. The company performs one-hot encoding on all non-numerical features in the training and validation datasets. The company uses the training dataset to train the classification model. When the company evaluates the model against the validation dataset, the results are less accurate than expected.
The company must improve the model’s accuracy on the validation dataset.
Which solution will meet this requirement?
A. Remove records that include outliers across all features.
B. Use a stratified split on the month and sales region features.
C. Perform normalization on the aggregate sales feature.
D. Perform normalization on the aggregate sales feature for each sales region.
Show Answer
Correct Answer: D
Explanation: The aggregate sales feature is on different scales across sales regions because regions have different numbers of stores. Global normalization does not remove these region-specific scale differences. Normalizing aggregate sales within each sales region preserves seasonal patterns while making values comparable across regions, improving the model's ability to generalize to the validation set. Removing outliers would discard valid seasonal peaks, and stratified splitting does not address the feature scaling issue.
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