A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train. The ML specialist wants to decrease the training time of the model.
Which approaches will meet this requirement? (Choose two.)

A. Replace On-Demand Instances with Spot Instances.

B. Configure model auto scaling dynamically to adjust the number of instances automatically.

C. Replace CPU-based EC2 instances with GPU-based EC2 instances.

D. Use multiple training instances.

E. Use a pre-trained version of the model. Run incremental training.

A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.

B. ׀¡onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook’s KMS role.

C. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.

D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening, pausing, and exiting the podcast. A machine learning (ML) specialist is designing the data ingestion of these events with the knowledge that the event payload needs some small transformations before inference.
How should the ML specialist design the data ingestion to meet these requirements with the LEAST operational overhead?

A. Ingest event data by using a GraphQLAPI in AWS AppSync. Store the data in an Amazon DynamoDB table. Use DynamoDB Streams to call an AWS Lambda function to transform the most recent 10 minutes of data before inference.

B. Ingest event data by using Amazon Kinesis Data Streams. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Glue to transform the most recent 10 minutes of data before inference.

C. Ingest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis Data Analytics for Apache Flink application to transform the most recent 10 minutes of data before inference.

D. Ingest event data by using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use an AWS Lambda function to transform the most recent 10 minutes of data before inference.

A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Choose two.)

A. Use Amazon SageMaker Ground Truth to sort the data into two groups named “enrolled” or “not enrolled.”

B. Use a forecasting algorithm to run predictions.

C. Use a regression algorithm to run predictions.

D. Use a classification algorithm to run predictions.

E. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named “enrolled” or “not enrolled.”

A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.
Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's needs? (Choose two.)

A. Add L1 regularization to the classifier

B. Add features to the dataset

C. Perform recursive feature elimination

D. Perform t-distributed stochastic neighbor embedding (t-SNE)

E. Perform linear discriminant analysis

A global company receives and processes hundreds of documents daily. The documents are in printed .pdf format or .jpg format.
A machine learning (ML) specialist wants to build an automated document processing workflow to extract text from specific fields from the documents and to classify the documents. The ML specialist wants a solution that requires low maintenance.
Which solution will meet these requirements with the LEAST operational effort?

A. Use a PaddleOCR model in Amazon SageMaker to detect and extract the required text and fields. Use a SageMaker text classification model to classify the document.

B. Use a PaddleOCR model in Amazon SageMaker to detect and extract the required text and fields. Use Amazon Comprehend to classify the document.

C. Use Amazon Textract to detect and extract the required text and fields. Use Amazon Rekognition to classify the document.

D. Use Amazon Textract to detect and extract the required text and fields. Use Amazon Comprehend to classify the document.

An online retailer collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.
The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.
Which combination of algorithms should the data scientist use to meet this requirement? (Choose two.)

A. Latent Dirichlet Allocation (LDA)

B. K-means

C. Semantic segmentation

D. Principal component analysis (PCA)

E. Factorization machines (FM)

A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?

A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.

B. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.

C. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.

D. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

A. Choose a higher number of layers

B. Choose a lower number of layers

C. Choose a smaller learning rate

D. Enable dropout

E. Include all the images from the test set in the training set

F. Enable early stopping

A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.
An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.
Which completion criteria will meet this requirement?

A. MaxRuntimeInSeconds

B. TargetObjectiveMetricValue

C. CompleteOnConvergence

D. MaxNumberOfTrainingJobsNotImproving

A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

A. Increase the number of S3 prefixes for the delivery stream to write to.

B. Decrease the retention period for the data stream.

C. Increase the number of shards for the data stream.

D. Add more consumers using the Kinesis Client Library (KCL).

A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.
Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions. The ML specialist must ensure that the data does not contain outliers before training the model.
How can the ML specialist meet these requirements with the LEAST operational overhead?

A. Load the data into an Amazon SageMaker Studio notebook. Calculate the first and third quartile. Use a SageMaker Data Wrangler data flow to remove only values that are outside of those quartiles.

B. Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset. Use a Data Wrangler data flow to remove outliers based on the bias report.

C. Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.

D. Use Amazon Lookout for Equipment to find and remove outliers from the dataset.

A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

A. Data scaling with standardization and normalization

B. Correlation plot with heat maps

C. Data binning

D. Univariate selection

E. Feature importance with a tree-based classifier

F. Data augmentation

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

A. Poisson distribution

B. Uniform distribution

C. Normal distribution

D. Binomial distribution

A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The
Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.

C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.

D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.

A healthcare company is using an Amazon SageMaker notebook instance to develop machine learning (ML) models. The company's data scientists will need to be able to access datasets stored in Amazon S3 to train the models. Due to regulatory requirements, access to the data from instances and services used for training must not be transmitted over the internet.
Which combination of steps should an ML specialist take to provide this access? (Choose two.)

A. Configure the SageMaker notebook instance to be launched with a VPC attached and internet access disabled.

B. Create and configure a VPN tunnel between SageMaker and Amazon S3.

C. Create and configure an S3 VPC endpoint Attach it to the VPC.

D. Create an S3 bucket policy that allows traffic from the VPC and denies traffic from the internet.

E. Deploy AWS Transit Gateway Attach the S3 bucket and the SageMaker instance to the gateway.

A company's data engineer wants to use Amazon S3 to share datasets with data scientists. The data scientists work in three departments: Finance. Marketing, and Human Resources. Each department has its own IAM user group. Some datasets contain sensitive information and should be accessed only by the data scientists from the Finance department.
How can the data engineer set up access to meet these requirements?

A. Create an S3 bucket for each dataset. Create an ACL for each S3 bucket. For each S3 bucket that contains a sensitive dataset, set the ACL to allow access only from the Finance department user group. Allow all three department user groups to access each S3 bucket that contains a non-sensitive dataset.

B. Create an S3 bucket for each dataset. For each S3 bucket that contains a sensitive dataset, set the bucket policy to allow access only from the Finance department user group. Allow all three department user groups to access each S3 bucket that contains a non-sensitive dataset.

C. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. For the Finance department user group, attach an IAM policy that provides access to both folders. For the Marketing and Human Resources department user groups, attach an IAM policy that provides access to only the folder that contains the non-sensitive datasets.

D. Create a single S3 bucket that includes two folders to separate the sensitive datasets from the non-sensitive datasets. Set the policy for the S3 bucket to allow only the Finance department user group to access the folder that contains the sensitive datasets. Allow all three department user groups to access the folder that contains the non-sensitive datasets.

A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR).
A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model.
Which solution will meet these requirements MOST cost-effectively?

A. Score the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an Amazon EC2 instance store volume and mount it to the Reserved Instances.

B. Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon FSx for Lustre volume and mount it to the Spot Instances.

C. Score the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved Instances. Create an Amazon EBS volume and mount it to the Reserved Instances.

D. Score the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create an Amazon Elastic File System (Amazon EFS) file system and mount it to the Spot Instances.

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.
 
Which model would have the HIGHEST accuracy?

A. Linear support vector machine (SVM)

B. Decision tree

C. Support vector machine (SVM) with a radial basis function kernel

D. Single perceptron with a Tanh activation function

A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.
 
How should the data scientist transform the data?

A. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.

C. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.

D. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.

B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

A data scientist is working on a model to predict a company's required inventory stock levels. All historical data is stored in .csv files in the company's data lake on Amazon S3. The dataset consists of approximately 500 GB of data The data scientist wants to use SQL to explore the data before training the model. The company wants to minimize costs.
Which option meets these requirements with the LEAST operational overhead?

A. Create an Amazon EMR cluster. Create external tables in the Apache Hive metastore, referencing the data that is stored in the S3 bucket. Explore the data from the Hive console.

B. Use AWS Glue to crawl the S3 bucket and create tables in the AWS Glue Data Catalog. Use Amazon Athena to explore the data.

C. Create an Amazon Redshift cluster. Use the COPY command to ingest the data from Amazon S3. Explore the data from the Amazon Redshift query editor GUI.

D. Create an Amazon Redshift cluster. Create external tables in an external schema, referencing the S3 bucket that contains the data. Explore the data from the Amazon Redshift query editor GUI.

A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.
The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.
Which approach will maximize transcription accuracy during the development phase?

A. Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.

B. Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocabulary file in all future transcription tasks.

C. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

D. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language model. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctly. Create an updated custom language model.

A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.
A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.
Which labeling approach will help the company improve this model?

A. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

B. Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

C. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.

D. Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?

A. Aggregate sales from stores in the same geographic area.

B. Apply smoothing to correct for seasonal variation.

C. Change the forecast frequency from daily to weekly.

D. Replace missing values in the dataset by using linear interpolation.

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population
How should the Data Scientist correct this issue?

A. Drop all records from the dataset where age has been set to 0.

B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset

C. Drop the age feature from the dataset and train the model using the rest of the features.

D. Use k-means clustering to handle missing features

A data engineer is using AWS Glue to create optimized, secure datasets in Amazon S3. The data science team wants the ability to access the ETL scripts directly from Amazon SageMaker notebooks within a VPC. After this setup is complete, the data science team wants the ability to run the AWS Glue job and invoke the
SageMaker training job.
Which combination of steps should the data engineer take to meet these requirements? (Choose three.)

A. Create a SageMaker development endpoint in the data science team’s VPC.

B. Create an AWS Glue development endpoint in the data science team’s VPC.

C. Create SageMaker notebooks by using the AWS Glue development endpoint.

D. Create SageMaker notebooks by using the SageMaker console.

E. Attach a decryption policy to the SageMaker notebooks.

F. Create an IAM policy and an IAM role for the SageMaker notebooks.

A company wants to predict the classification of documents that are created from an application. New documents are saved to an Amazon S3 bucket every 3 seconds. The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document.
Which approach will meet these requirements with the LEAST operational overhead?

A. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document.

B. Deploy all the models to a single SageMaker endpoint. Treat each model as a production variant. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each production variant and return the results of each model.

C. Deploy each model to its own SageMaker endpoint Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each endpoint and return the results of each model.

D. Deploy each model to its own SageMaker endpoint. Create three AWS Lambda functions. Configure each Lambda function to call a different endpoint and return the results. Configure three S3 event notifications to invoke the Lambda functions when new documents are created.

A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:
 
The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

A. Apply integer transformation and set Red = 1, White = 5, and Green = 10.

B. Add new columns that store one-hot representation of colors.

C. Replace the color name string by its length.

D. Create three columns to encode the color in RGB format.

E. Replace each color name by its training set frequency.

A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?

A. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

B. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

C. Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

D. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

A Machine Learning Specialist needs to move and transform data in preparation for training. Some of the data needs to be processed in near-real time, and other data can be moved hourly. There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.
Which of the following services can feed data to the MapReduce jobs? (Choose two.)

A. AWS DMS

B. Amazon Kinesis

C. AWS Data Pipeline

D. Amazon Athena

E. Amazon ES

An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to make changes to applications that rely on the API. The company also wants to evaluate the performance of the new model in production traffic before the company fully rolls out the new model to all users.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a new SageMaker endpoint for the new model. Configure an Application Load Balancer (ALB) to distribute traffic between the old model and the new model.

B. Modify the existing endpoint to use SageMaker production variants to distribute traffic between the old model and the new model.

C. Modify the existing endpoint to use SageMaker batch transform to distribute traffic between the old model and the new model.

D. Create a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB) to distribute traffic between the old model and the new model.

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.
The solution needs to do the following:
✑ Calculate an anomaly score for each web traffic entry.
Adapt unusual event identification to changing web patterns over time.
 
Which approach should the data scientist implement to meet these requirements?

A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record.

B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.

C. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.

D. Collect the streaming data using Amazon Kinesis Data Firehose. Map the delivery stream as an input source for Amazon Kinesis Data Analytics. Write a SQL query to run in real time against the streaming data with the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

A. Linear regression

B. Classification

C. Clustering

D. Reinforcement learning

An analytics company has an Amazon SageMaker hosted endpoint for an image classification model. The model is a custom-built convolutional neural network (CNN) and uses the PyTorch deep learning framework. The company wants to increase throughput and decrease latency for customers that use the model.
Which solution will meet these requirements MOST cost-effectively?

A. Use Amazon Elastic Inference on the SageMaker hosted endpoint.

B. Retrain the CNN with more layers and a larger dataset.

C. Retrain the CNN with more layers and a smaller dataset.

D. Choose a SageMaker instance type that has multiple GPUs.

An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items.
A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.
How should the data scientist meet these requirements MOST cost-effectively?

A. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {“HyperParameterTuningJobObjective”: {“MetricName”: “validation:accuracy”, “Type”: “Maximize”}}.

B. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {“HyperParameterTuningJobObjective”: {“MetricName”: “validation’ll”, “Type”: “Maximize”}}.

C. Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {“HyperParameterTuningJobObjective”: {“MetricName”: “validation:f1”, “Type”: “Maximize”}}.

D. Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {“HyperParameterTuningJobObjective”: {“MetricName”: “validation:f1”, “Type”: “Minimize”}}.

A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric. The ML specialist varies the number and size of the MLP's hidden layers, but the results do not improve significantly.
Which solution will improve recall in the LEAST amount of time?

A. Add class weights to the MLP’s loss function, and then retrain.

B. Gather more data by using Amazon Mechanical Turk, and then retrain.

C. Train a k-means algorithm instead of an MLP.

D. Train an anomaly detection model instead of an MLP.

A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ׀¢׀’ of training data that consists of labeled images of defective product parts. The training data is in the corporate on- premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.

B. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.

C. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

D. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

The displayed graph is from a forecasting model for testing a time series.
 
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

A. The model predicts both the trend and the seasonality well

B. The model predicts the trend well, but not the seasonality.

C. The model predicts the seasonality well, but not the trend.

D. The model does not predict the trend or the seasonality well.

A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.
The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store. The data scientist needs to prepare the new historic data for training and inference by using native integrations.
Which solution will meet these requirements with the LEAST development effort?

A. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.

B. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.

C. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.

D. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing
(NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

A. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.

B. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.

C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

D. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon
S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?

A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

A manufacturing company wants to monitor its devices for anomalous behavior. A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry. The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour. The total time to run the model across the telemetry for all devices is 5 minutes.
What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?

A. SageMaker Batch Transform

B. SageMaker Asynchronous Inference

C. SageMaker Processing

D. A SageMaker multi-container endpoint

A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:
•	True positive rate (TPR): 0.700
•	False negative rate (FNR): 0.300
•	True negative rate (TNR): 0.977
•	False positive rate (FPR): 0.023
•	Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?

A. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

B. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

C. Undersample the minority class.

D. Oversample the majority class.

A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores. The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

A. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3. Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.

B. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.

C. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table. Write the results back to Amazon S3.

D. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift table. Merge the data in Amazon Redshift. Write the results back to Amazon S3.

A company has hired a data scientist to create a loan risk model. The dataset contains loan amounts and variables such as loan type, region, and other demographic variables. The data scientist wants to use Amazon SageMaker to test bias regarding the loan amount distribution with respect to some of these categorical variables.
Which pretraining bias metrics should the data scientist use to check the bias distribution? (Choose three.)

A. Class imbalance

B. Conditional demographic disparity

C. Difference in proportions of labels

D. Jensen-Shannon divergence

E. Kullback-Leibler divergence

F. Total variation distance

A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.
Which Amazon SageMaker algorithm will meet this requirement?

A. XGBoost

B. Image Classification – TensorFlow

C. Object Detection – TensorFlow

D. Semantic segmentation – MXNet

A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?

A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.

C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.

D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to, pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.
How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

A. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.

B. Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

C. Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.

D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.