ExamGecko
Search Search Login
Home Home / Amazon / MLS-C01

Amazon MLS-C01 Practice Test - Questions Answers, Page 19

Question list
Search
Search

List of questions

Search
Open VPLUS File
Convert VPLUS to PDF

Related questions

A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company: * Solution simplicity * Fast development time * Low cost * High flexibility What technologies meet the company's requirements?
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows: ...traction Timestamp (Timeslamp) ...JName(Varchar) ...JNo (Varchar) Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard. The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster Which solution meets these requirements?
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome. The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years. The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance. Which solution will meet these requirements?
A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials. ...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%. ...should the company retrain the model to meet these requirements?
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application. The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

Question 181

Report
Export
Collapse

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.
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.
Answers
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.
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.
Answers
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.
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.
Answers
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.
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.
Answers
Suggested answer: D

Explanation:

Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data in real time using SQL queries. Amazon Random Cut Forest (RCF) is a SQL extension that enables anomaly detection on streaming data. RCF is an unsupervised machine learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. A sliding window is a type of window that moves along with the data stream, so that the anomaly detection model can adapt to changing patterns over time. A tumbling window is a type of window that has a fixed size and does not overlap with other windows, so that the anomaly detection model is based on a fixed period of time. Therefore, option D is the best approach to meet the requirements of the question, as it uses RCF to calculate anomaly scores for each web traffic entry and uses a sliding window to adapt to changing web patterns over time.

Option A is incorrect because Amazon SageMaker Random Cut Forest (RCF) is a built-in model that can be used to train and deploy anomaly detection models on batch or streaming data, but it requires more steps and resources than using the RCF SQL extension in Amazon Kinesis Data Analytics. Option B is incorrect because Amazon SageMaker XGBoost is a built-in model that can be used for supervised learning tasks such as classification and regression, but not for unsupervised learning tasks such as anomaly detection. Option C is incorrect because k-Nearest Neighbors (kNN) is a SQL extension that can be used for classification and regression tasks on streaming data, but not for anomaly detection. Moreover, using a tumbling window would not allow the anomaly detection model to adapt to changing web patterns over time.

References:

Using CloudWatch anomaly detection

Anomaly Detection With CloudWatch

Performing Real-time Anomaly Detection using AWS

What Is AWS Anomaly Detection? (And Is There A Better Option?)

Question 182

Report
Export
Collapse

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.

What type of machine learning model should be used?

A.
Classification month-to-month using supervised learning of the 200 categories based on claim contents.
A.
Classification month-to-month using supervised learning of the 200 categories based on claim contents.
Answers
B.
Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
B.
Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
Answers
C.
Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
C.
Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
Answers
D.
Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
D.
Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
Answers
Suggested answer: C

Explanation:

: Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.

Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.

References:

Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library

Time Series Forecasting Service -- Amazon Forecast -- Amazon Web Services

Amazon Forecast: Guide to Predicting Future Outcomes - Onica

Amazon Launches What-If Analyses for Machine Learning Forecasting ...

Question 183

Report
Export
Collapse

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.

The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.

Which solution satisfies these requirements with MINIMAL effort?

A.
Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
A.
Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
Answers
B.
Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
B.
Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
Answers
C.
Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
C.
Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
Answers
D.
Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
D.
Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
Answers
Suggested answer: B

Explanation:

Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.

Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.

References:

Deploying models to Amazon SageMaker hosting services - Amazon SageMaker

Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker

UpdateEndpointWeightsAndCapacities - Amazon SageMaker

Question 184

Report
Export
Collapse

An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.

The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras.

Which approach should a Machine Learning Specialist take to obtain accurate predictions?

A.
Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
A.
Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
Answers
B.
Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
B.
Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
Answers
C.
Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
C.
Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) algorithm.
Answers
D.
Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
D.
Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
Answers
Suggested answer: C

Explanation:

The problem of detecting specific types of weeds and their location within the field is an example of object detection, which is a type of machine learning model that identifies and localizes objects in an image. Amazon SageMaker provides a built-in object detection algorithm that uses a single-shot multibox detector (SSD) to perform real-time inference on streaming images. The SSD algorithm can handle multiple objects of varying sizes and scales in an image, and generate bounding boxes and scores for each object category. Therefore, option C is the best approach to obtain accurate predictions.

Option A is incorrect because image classification is a type of machine learning model that assigns a label to an image based on predefined categories. Image classification is not suitable for localizing objects within an image, as it does not provide bounding boxes or scores for each object. Option B is incorrect because Apache Parquet is a columnar storage format that is optimized for analytical queries. Apache Parquet is not suitable for storing images, as it does not preserve the spatial information of the pixels. Option D is incorrect because it combines the wrong format (Apache Parquet) and the wrong algorithm (image classification) for the given problem, as explained in options A and B.

References:

Object Detection algorithm now available in Amazon SageMaker

Image classification and object detection using Amazon Rekognition Custom Labels and Amazon SageMaker JumpStart

Object Detection with Amazon SageMaker - W3Schools aws-samples/amazon-sagemaker-tensorflow-object-detection-api

Question 185

Report
Export
Collapse

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.

To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.

Which deployment architecture for the model will address these business requirements?

A.
Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
A.
Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
Answers
B.
Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
B.
Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
Answers
C.
Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
C.
Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
Answers
D.
Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
D.
Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
Answers
Suggested answer: B

Explanation:

AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.

Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low-latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.

References:

AWS Greengrass Machine Learning Inference - Amazon Web Services

Machine learning components - AWS IoT Greengrass

What is AWS Greengrass? | AWS IoT Core | Onica

GitHub - aws-samples/aws-greengrass-ml-deployment-sample

AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

Question 186

Report
Export
Collapse

A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.

Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

A.
Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
A.
Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
Answers
B.
Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
B.
Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
Answers
C.
Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
C.
Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
Answers
D.
Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
D.
Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
Answers
Suggested answer: B

Explanation:

Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker's prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.

Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.

References:

Bring your own model with Amazon SageMaker script mode

GitHub - aws-samples/amazon-sagemaker-script-mode

Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3

amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master

Question 187

Report
Export
Collapse

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.

Which machine learning algorithm should the researchers use that BEST meets their requirements?

A.
Latent Dirichlet Allocation (LDA)
A.
Latent Dirichlet Allocation (LDA)
Answers
B.
Recurrent neural network (RNN)
B.
Recurrent neural network (RNN)
Answers
C.
K-means
C.
K-means
Answers
D.
Convolutional neural network (CNN)
D.
Convolutional neural network (CNN)
Answers
Suggested answer: D

Explanation:

The problem of detecting whether or not individuals in a collection of images are wearing the company's retail brand is an example of image recognition, which is a type of machine learning task that identifies and classifies objects in an image. Convolutional neural networks (CNNs) are a type of machine learning algorithm that are well-suited for image recognition, as they can learn to extract features from images and handle variations in size, shape, color, and orientation of the objects. CNNs consist of multiple layers that perform convolution, pooling, and activation operations on the input images, resulting in a high-level representation that can be used for classification or detection. Therefore, option D is the best choice for the machine learning algorithm that meets the requirements of the chief editor.

Option A is incorrect because latent Dirichlet allocation (LDA) is a type of machine learning algorithm that is used for topic modeling, which is a task that discovers the hidden themes or topics in a collection of text documents. LDA is not suitable for image recognition, as it does not preserve the spatial information of the pixels. Option B is incorrect because recurrent neural networks (RNNs) are a type of machine learning algorithm that are used for sequential data, such as text, speech, or time series. RNNs can learn from the temporal dependencies and patterns in the input data, and generate outputs that depend on the previous states. RNNs are not suitable for image recognition, as they do not capture the spatial dependencies and patterns in the input images. Option C is incorrect because k-means is a type of machine learning algorithm that is used for clustering, which is a task that groups similar data points together based on their features. K-means is not suitable for image recognition, as it does not perform classification or detection of the objects in the images.

References:

Image Recognition Software - ML Image & Video Analysis - Amazon ...

Image classification and object detection using Amazon Rekognition ...

AWS Amazon Rekognition - Deep Learning Face and Image Recognition ...

GitHub - awslabs/aws-ai-solution-kit: Machine Learning APIs for common ...

Meet iNaturalist, an AWS-powered nature app that helps you identify ...

Question 188

Report
Export
Collapse

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

A.
Use the event tracker in Amazon Personalize to include real-time user interactions.
A.
Use the event tracker in Amazon Personalize to include real-time user interactions.
Answers
B.
Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
B.
Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
Answers
C.
Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
C.
Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
Answers
D.
Add event type and event value fields to the interactions dataset in Amazon Personalize.
D.
Add event type and event value fields to the interactions dataset in Amazon Personalize.
Answers
Suggested answer: A

Explanation:

The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user's preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.

The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN-Metadata recipe in Amazon Personalize can help capture the user's attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.

References:

Recording events - Amazon Personalize

Using real-time events - Amazon Personalize

Question 189

Report
Export
Collapse

A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.

Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

A.
Add a VPC endpoint policy to allow access to the IAM users.
A.
Add a VPC endpoint policy to allow access to the IAM users.
Answers
B.
Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
B.
Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
Answers
C.
Modify the security group on the endpoint network interface to restrict access to the instances.
C.
Modify the security group on the endpoint network interface to restrict access to the instances.
Answers
D.
Modify the ACL on the endpoint network interface to restrict access to the instances.
D.
Modify the ACL on the endpoint network interface to restrict access to the instances.
Answers
E.
Add a SageMaker Runtime VPC endpoint interface to the VPC.
E.
Add a SageMaker Runtime VPC endpoint interface to the VPC.
Answers
Suggested answer: C, E

Explanation:

To secure calls to the Amazon SageMaker Service API, the ML specialist should take the following steps:

Modify the security group on the endpoint network interface to restrict access to the instances. This will allow the ML specialist to control which instances in the VPC can communicate with the VPC interface endpoint for the Amazon SageMaker Service API.The security group can specify inbound and outbound rules based on the instance IDs, IP addresses, or CIDR blocks1.

Add a SageMaker Runtime VPC endpoint interface to the VPC. This will allow the ML specialist to invoke the SageMaker endpoints from within the VPC without using the public internet.The SageMaker Runtime VPC endpoint interface connects the VPC directly to the SageMaker Runtime using AWS PrivateLink2.

The other options are not as effective or necessary as the steps above. Adding a VPC endpoint policy to allow access to the IAM users is not required, as the IAM users can already access the Amazon SageMaker Service API through the VPC interface endpoint. Modifying the users' IAM policy to allow access to Amazon SageMaker Service API calls only is not sufficient, as it does not prevent unauthorized instances from accessing the VPC interface endpoint.Modifying the ACL on the endpoint network interface to restrict access to the instances is not possible, as network ACLs are associated with subnets, not network interfaces3.

References:

Security groups for your VPC - Amazon Virtual Private Cloud

Connect to SageMaker Within your VPC - Amazon SageMaker

Network ACLs - Amazon Virtual Private Cloud

Question 190

Report
Export
Collapse

An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.

How should a machine learning specialist meet these requirements?

A.
Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
A.
Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
Answers
B.
Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
B.
Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
Answers
C.
Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
C.
Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
Answers
D.
Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
D.
Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
Answers
Suggested answer: C

Explanation:

The best option is to use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3. This option meets the following requirements:

It ensures that only nonsensitive data is transferred to the cloud by using table mapping to filter out the tables that contain sensitive data1.

It uses IPsec to secure the data transfer by enabling SSL encryption for the AWS DMS endpoint2.

It uploads the data to Amazon S3 each day for model retraining by using the ongoing replication feature of AWS DMS3.

The other options are not as effective or feasible as the option above. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest data through an AWS Site-to-Site VPN connection directly into Amazon S3 is possible, but it requires more steps and resources than using AWS DMS. Also, it does not specify how to filter out the sensitive data from the tables. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job is also possible, but it is more complex and error-prone than using AWS DMS. Also, it does not use IPsec as required.Using PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection, and then using AWS Glue to move data from Amazon EC2 to Amazon S3 is not feasible, because PostgreSQL logical replication does not support replicating only a subset of data4. Also, it involves unnecessary data movement and additional costs.

References:

Table mapping - AWS Database Migration Service

Using SSL to encrypt a connection to a DB instance - AWS Database Migration Service

Ongoing replication - AWS Database Migration Service

Logical replication - PostgreSQL

Total 308 questions
Go to page: of 31
ExamGecko

Copyright @2023 ExamGecko | All right reserved

Mail us: [email protected]
About us
Contact us
Twitter X
Facebook
Medium
Convert VPLUS to PDF
Open VPLUS files Easily and Quickly
Privacy Policy
Disclaimer
Terms