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Question # 1
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required. What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?
A. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.
B. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
C. Create a SageMaker processing job by calling the SageMaker Python SDK.
D. Create a data flow in SageMaker Data Wrangler. Configure a transform step.
Question # 2
Case study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data. Which solution will meet this requirement with the LEAST operational effort?
A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
C. Use AWS Glue DataBrew built-in features to oversample the minority class.
D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
Question # 3
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results. An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs. Which solution will meet these requirements?
A. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.
B. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
C. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
D. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
Question # 4
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support. Which modeling approach should the company use to meet this requirement?
A. Anomaly detection
B. Linear regression
C. Logistic regression
D. Semantic segmentation
Question # 5
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor. Which solution will meet this requirement?
A. Configure the competitor's name as a blocked phrase in Amazon Q Business.
B. Configure an Amazon Q Business retriever to exclude the competitor’s name.
C. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
Question # 6
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.Which solutions will mitigate this problem? (Choose two.)
A. Enable early stopping on the model.
B. Increase dropout in the layers.
C. Increase the number of layers.
D. Increase the number of neurons.
E. Investigate and reduce the sources of model bias.
Question # 7
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 in size and consists of CSV, JSON, Apache Parquet, and simple text files. The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated. Which solution will meet these requirements?
A. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
B. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
C. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
Question # 8
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?
A. AWS::SageMaker::Model
B. AWS::SageMaker::Endpoint
C. AWS::SageMaker::NotebookInstance
D. AWS::SageMaker::Pipeline
Question # 9
An ML engineer needs to use an ML model to predict the price of apartments in a specific location. Which metric should the ML engineer use to evaluate the model's performance?
A. Accuracy
B. Area Under the ROC Curve (AUC)
C. F1 score
D. Mean absolute error (MAE)
Question # 10
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance. Which solution will meet these requirements in the LEAST amount of time?
A. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
B. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
C. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
D. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.
Question # 11
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance. The company needs to centralize management of the team's permissions. Which solution will meet this requirement?
A. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
B. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
C. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
D. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.
Question # 12
A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months. Which solution will meet this requirement?
A. Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.
B. Store the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
C. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.
D. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.
Question # 13
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score. During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly. What could be the reason for the reduced F1 score?
A. Concept drift occurred in the underlying customer data that was used for predictions.
B. The model was not sufficiently complex to capture all the patterns in the original baseline data.
C. The original baseline data had a data quality issue of missing values.
D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.
Question # 14
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate. During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions. What should the ML engineer do to improve the fraud detection for new transactions?
A. Increase the learning rate.
B. Remove some irrelevant features from the training dataset.
C. Increase the value of the max_depth hyperparameter.
D. Decrease the value of the max_depth hyperparameter.
Question # 15
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances. What should the ML engineer do to MINIMIZE the communication overhead between the instances?
A. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
B. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
C. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
D. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.
Question # 16
A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker. An ML engineer must make the training data accessible for ML models that are in the SageMaker environment. Which solution will meet these requirements?
A. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
B. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
C. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
D. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
Question # 17
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 catalogthe models.
D. Use the SageMaker Model Registry and unique tags for each model version.
Question # 18
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks. What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?
A. Adjust the model's parameters and hyperparameters.
B. Initiate a manual Model Monitor job that uses the most recent production data.
C. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
D. Include additional data in the existing training set for the model. Retrain and redeploy the model.
Question # 19
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data. Which file format will meet these requirements?
A. CSV files compressed with Snappy
B. JSON objects in JSONL format
C. JSON files compressed with gzip
D. Apache Parquet files
Question # 20
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket. Which solution will meet these requirements with the LEAST operational effort?
A. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
B. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
C. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.
Question # 21
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents. Which solution will meet these requirements with the LEAST operational overhead?
A. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text preprocessing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
B. Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
C. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
D. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.
Question # 22
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents. The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras. Which solution will improve the model's accuracy in the LEAST amount of time?
A. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
B. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
C. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
D. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
Question # 23
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random. Which solution will meet these requirements?
A. Increase the temperature parameter and the top_k parameter.
B. Increase the temperature parameter. Decrease the top_k parameter.
C. Decrease the temperature parameter. Increase the top_k parameter.
D. Decrease the temperature parameter and the top_k parameter.
Question # 24
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups. The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker. Which solution will provide the ML engineers with the appropriate access?
A. Enable S3 bucket versioning.
B. Configure S3 Object Lock settings for each user.
C. Add cross-origin resource sharing (CORS) policies to the S3 buckets.
D. Create IAM policies. Attach the policies to IAM users or IAM roles.
Question # 25
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets. Which solution will meet these requirements?
A. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
C. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.
Question # 26
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions. Which metric finding should the ML engineer prioritize the MOST when choosing the model?
A. Low precision
B. High precision
C. Low recall
D. High recall
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