HOTSPOT -
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
• Store the resulting data back in Amazon S3.
• Use Amazon Athena to infer the schemas and available columns.
• Use AWS Glue crawlers to infer the schemas and available columns.
• Use AWS Glue DataBrew for data cleaning and feature engineering.
Explanation
AWS Glue crawlers are used to infer schemas from the .csv files stored in Amazon S3, which is essential given that the columns are not labeled and sparsely populated. AWS Glue DataBrew then enables the ML engineer to perform necessary data cleaning and feature engineering tasks on this inferred schema. Finally, storing the processed data back in Amazon S3 ensures it is ready for use in training ML models.
HOTSPOT -
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
• Access the store to build datasets for training.
• Create a feature group.
• Ingest the records.
Explanation
According to AWS documentation, creating and using features in Amazon SageMaker Feature Store involves first defining a schema by creating a feature group, then ingesting data into this defined structure, and finally accessing the stored features to build training datasets. This sequence ensures that the feature store is properly configured before data ingestion and usage for model training.
HOTSPOT -
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (CI/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
• An S3 event notification invokes the pipeline when new data is uploaded.
• An S3 Lifecycle rule invokes the pipeline when new data is uploaded.
• SageMaker retrains the model by using the data in the S3 bucket.
• The pipeline deploys the model to a SageMaker endpoint.
• The pipeline deploys the model to SageMaker Model Registry.
Explanation
Step 1: An S3 event notification fires when new objects are uploaded, so it can start the pipeline automatically when new training data arrives. S3 Lifecycle rules only transition or expire objects; they cannot invoke a pipeline.
Step 2: SageMaker retrains the model by using the new data in the S3 bucket, so the model reflects the latest data.
Step 3: The pipeline deploys the retrained model to a SageMaker endpoint, which is what hosts the model and serves predictions. Model Registry only catalogs and versions model packages; registering a model there does not host it.
HOTSPOT -
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
• Embedding
• Retrieval Augmented Generation (RAG)
• Temperature
• Token
Explanation
According to official AWS documentation, tokens are the basic units of text processed by LLMs, making them the correct representation for fundamental data elements. Embeddings are high-dimensional vectors that capture the semantic meaning of text, aligning with their description as containing textual semantics in a mathematical form. Retrieval Augmented Generation (RAG) enhances generated responses by incorporating information from external sources, fitting the description of enriching responses with additional data.
HOTSPOT -
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
• Feature splitting
• Logarithmic transformation
• One-hot encoding
• Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
Explanation
One-hot encoding is appropriate for categorical data like city names, which cannot be ordered or measured numerically. Feature splitting is suitable for composite features such as "Type_year," where separating the type of home and the year it was built can provide clearer insights to the model. Logarithmic transformation is effective for size measurements, as it helps in handling skewed distributions often found with area measurements, making patterns more visible to the model.
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.Correct
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.
Explanation
The correct answer is A because concept drift refers to changes in the underlying patterns of the data over time, which can lead to a decrease in model performance metrics like the F1 score. This aligns with AWS documentation on monitoring and maintaining machine learning models where concept drift is recognized as a common issue affecting model accuracy over time.
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.Correct
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.
Explanation
The correct answer is A because creating a single IAM role with necessary permissions and attaching it to each SageMaker notebook instance centralizes permission management for the data scientists' team, as per AWS best practices. This approach ensures consistent access control without needing individual roles for each user or notebook instance.
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)Correct
Explanation
The correct answer is D, Mean Absolute Error (MAE). This metric is appropriate for regression tasks where the goal is to predict a continuous value, such as apartment prices. MAE measures the average magnitude of errors between predicted and actual values without considering their direction, making it suitable for evaluating model performance in this context.
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?
A.Introduce early stopping.
B.Increase the size of the test set.
C.Increase the learning rate.
D.Decrease the learning rate.Correct
Explanation
The correct answer is D because decreasing the learning rate can help stabilize the training process and reduce the oscillating pattern observed in both training loss and validation loss, leading to potentially better performance on the test set according to AWS documentation.
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?
A.Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.Correct
B.Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
C.Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.
D.Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
Explanation
Option A is correct because using Amazon Athena with a CREATE TABLE AS SELECT (CTAS) statement allows for direct querying of CSV objects stored in an S3 bucket without requiring additional processing or setup. This approach minimizes operational overhead as it leverages the existing data and infrastructure, enabling efficient querying based on the transaction date.
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?
A.Use Amazon Mechanical Turk jobs to detect duplicates.
B.Use Amazon QuickSight ML Insights to build a custom deduplication model.
C.Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
D.Use the AWS Glue FindMatches transform to detect duplicates.Correct
Explanation
The correct answer is D because AWS Glue's FindMatches transform is specifically designed for detecting duplicate records in datasets with minimal code development, aligning with the requirement of least code development. This feature leverages machine learning to identify and group similar records based on specified attributes without needing extensive custom coding or manual intervention.
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?
A.Spot InstancesCorrect
B.Reserved Instances
C.On-Demand Instances
D.Dedicated Instances
Explanation
Spot Instances use spare EC2 capacity at a steep discount compared with On-Demand prices, and AWS can reclaim them with a two-minute warning. Because this 90-minute weekend batch job can tolerate interruptions, Spot is the most cost-effective option. Reserved Instances require a 1- or 3-year commitment and would sit idle most of the week, On-Demand costs more, and Dedicated Instances add cost for hardware isolation that is not required.
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region.
Which solution will meet this requirement with the LEAST development effort?
A.Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
B.Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.Correct
C.Use AWS DataSync to replicate the model from Account A to Account B.
D.Create an AWS Site-to-Site VPN connection between Account A and Account B to transfer the model.
Explanation
The correct answer is B because it leverages Amazon Comprehend's ImportModel API operation, which is specifically designed for copying models across accounts with minimal development effort. Creating a resource-based IAM policy ensures that the necessary permissions are in place to perform this action between different AWS accounts.
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.Correct
B.Increase dropout in the layers.Correct
C.Increase the number of layers.
D.Increase the number of neurons.
E.Investigate and reduce the sources of model bias.
Explanation
The correct answers are A and B because enabling early stopping (A) can prevent overfitting by halting training when validation performance starts to degrade, while increasing dropout in the layers (B) helps mitigate overfitting by randomly setting a fraction of input units to 0 at each update during training time, which helps reduce co-adaptation on training data.
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?
A.Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
B.Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
C.Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.Correct
D.Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.
Explanation
Amazon Kendra is specifically designed for indexing and searching text-based content, including performing semantic search on ingested documents. By using the Amazon Kendra S3 connector, the application can ingest documents from an S3 bucket directly into Amazon Kendra, enabling efficient semantic searches as required.
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?
A.Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.Correct
B.Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
C.Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
D.Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.
Explanation
Amazon SageMaker Autopilot automates the model creation process, including data preprocessing and model tuning, which minimizes development effort compared to manually implementing scripts or using services not designed for automated machine learning model creation.
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?
A.Apply label encoding to the color categories. Automatically assign each color a unique integer.
B.Implement padding to ensure that all color feature vectors have the same length.
C.Perform dimensionality reduction on the color categories.
D.One-hot encode the color categories to transform the color scheme feature into a binary matrix.Correct
Explanation
One-hot encoding (Option D) is the correct technique for transforming categorical data like color schemes into a format that can be provided to machine learning algorithms. This method converts each category value into a new column and assigns a 1 or 0 (binary) value to the column, indicating the presence or absence of the category in the row. This approach avoids imposing an arbitrary ordering on categories as label encoding might do, which is crucial for categorical data without intrinsic order.
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon S3 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
A.Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
B.Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
C.Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.Correct
D.Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
Explanation
Amazon Macie is specifically designed to automatically discover, classify, and protect sensitive data in AWS environments, making it the most efficient choice for identifying sensitive data with minimal operational overhead. Using AWS Lambda functions alongside Macie further reduces operational complexity by automating the process of removing identified sensitive data without requiring the management of servers or clusters.
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.Correct
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.
Explanation
AWS Glue is designed for data ingestion and ETL processes, making it suitable for creating data ingestion pipelines from S3 buckets. Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models, thus meeting the requirement for model deployment pipelines.
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?
A.Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
B.Create a model group for each category. Move the existing models into these category model groups.
C.Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
D.Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.Correct
Explanation
Option D is correct because creating a Model Registry collection for each category allows organizing the existing models without affecting their integrity or existing groupings, thus improving discoverability at scale as required.
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