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MLS-C01 — questions

Page 13 of 19 · 369 total questions.

MLS-C01 Question 241

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Topic 1 · Question 241

Each morning, a data scientist at a rental car company creates insights about the previous day’s rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company’s locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data. Which solution will meet these requirements with the LEAST development time?

  • AUse Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight. (correct answer)
  • BUse Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
  • CUse Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
  • DUse Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Kinesis Data Firehose delivers streaming data to destinations like S3/Redshift with no servers to manage. Amazon Kinesis ingests and processes real-time streaming data at scale. This option meets the real-time / low-latency performance requirement.

MLS-C01 Question 242

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Topic 1 · Question 242

A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference. The repository is hosted in Amazon SageMaker Feature Store. The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time. Which solution will meet these requirements?

  • AUse the SageMaker Feature Store BatchGetRecord API with the record identifier. Filter to find the latest record.
  • BCreate an Amazon Athena query to retrieve the data from the feature table.
  • CCreate an Amazon Athena query to retrieve the data from the feature table. Use the write_time value to find the latest record.
  • DUse the SageMaker Feature Store GetRecord API with the record identifier. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use the SageMaker Feature Store GetRecord API with the record identifier.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option meets the real-time / low-latency performance requirement.

MLS-C01 Question 243

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Topic 1 · Question 243 · Select all that apply

A company’s data scientist has trained a new machine learning model that performs better on test data than the company’s existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data. The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data. Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

  • ACreate a new endpoint configuration that includes a production variant for each of the two models. (correct answer)
  • BCreate a new endpoint configuration that includes two target variants that point to different endpoints.
  • CDeploy the new model to the existing endpoint.
  • DUpdate the existing endpoint to activate the new model.
  • EUpdate the existing endpoint to use the new endpoint configuration. (correct answer)
Reveal answer & explanation
Correct answer: A, E

The correct answer is A, E. Option A: Create a new endpoint configuration that includes a production variant for each of the two models. Option E: Update the existing endpoint to use the new endpoint configuration.

Explanation

AWS Config tracks resource configuration changes and evaluates compliance.

MLS-C01 Question 244

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Topic 1 · Question 244

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format: March 1st, 2020, 08:14pm - There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis. Which solution requires the LEAST operational overhead to create a new dataset with the added features?

  • ACreate an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
  • BCreate a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
  • CCreate a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3. (correct answer)
  • DCreate an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLS-C01 Question 245

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Topic 1 · Question 245

A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results. Which modeling approach will deliver the MOST accurate prediction of product quality?

  • AAmazon SageMaker DeepAR forecasting algorithm
  • BAmazon SageMaker XGBoost algorithm (correct answer)
  • CAmazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
  • DA convolutional neural network (CNN) and ResNet
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Amazon SageMaker XGBoost algorithm

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLS-C01 Question 246

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Topic 1 · Question 246

A healthcare company wants to create a machine learning (ML) model to predict patient outcomes. A data science team developed an ML model by using a custom ML library. The company wants to use Amazon SageMaker to train this model. The data science team creates a custom SageMaker image to train the model. When the team tries to launch the custom image in SageMaker Studio, the data scientists encounter an error within the application. Which service can the data scientists use to access the logs for this error?

  • AAmazon S3
  • BAmazon Elastic Block Store (Amazon EBS)
  • CAWS CloudTrail
  • DAmazon CloudWatch (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Amazon CloudWatch

Explanation

Amazon CloudWatch collects metrics, logs and alarms to monitor and react to operational health.

MLS-C01 Question 247

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Topic 1 · Question 247

A data scientist wants to build a financial trading bot to automate investment decisions. The financial bot should recommend the quantity and price of an asset to buy or sell to maximize long-term profit. The data scientist will continuously stream financial transactions to the bot for training purposes. The data scientist must select the appropriate machine learning (ML) algorithm to develop the financial trading bot. Which type of ML algorithm will meet these requirements?

  • ASupervised learning
  • BUnsupervised learning
  • CSemi-supervised learning
  • DReinforcement learning (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Reinforcement learning.

MLS-C01 Question 248

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Topic 1 · Question 248

A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model. Which TensorFlow estimator configuration will train the model MOST cost-effectively?

  • ATurn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • BTurn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method. (correct answer)
  • CAdjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • DTurn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimato...

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. AWS Config tracks resource configuration changes and evaluates compliance. This option delivers the requirement at the lowest cost.

MLS-C01 Question 249

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Topic 1 · Question 249

An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK. The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy. Which solution will improve the computational efficiency of the models?

  • AUse Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
  • BUse Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
  • CUse Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model. (correct answer)
  • DUse Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-rankin...

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

MLS-C01 Question 250

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Topic 1 · Question 250 · Select all that apply

A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset. Which steps must the data scientist take to improve model accuracy? (Choose three.)

  • AIncrease the amount of regularization that the model uses.
  • BDecrease the amount of regularization that the model uses. (correct answer)
  • CIncrease the number of training examples that that model uses. (correct answer)
  • DIncrease the number of test examples that the model uses.
  • EIncrease the number of model features that the model uses. (correct answer)
  • FDecrease the number of model features that the model uses.
Reveal answer & explanation
Correct answer: B, C, E

The correct answer is B, C, E. Option B: Decrease the amount of regularization that the model uses. Option C: Increase the number of training examples that that model uses. Option E: Increase the number of model features that the model uses.

MLS-C01 Question 251

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Topic 1 · Question 251

A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car. Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

  • AUse a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
  • BUse a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car. (correct answer)
  • CUse a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
  • DUse a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
Reveal answer & explanation
Correct answer: B

The correct answer is B. Option B: Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.

MLS-C01 Question 252

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Topic 1 · Question 252 · Select all that apply

A company is creating an application to identify, count, and classify animal images that are uploaded to the company’s website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common. The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker. Which combination of steps should the ML engineer take to train the model? (Choose two.)

  • AUse a ResNet model. Initiate full training mode by initializing the network with random weights.
  • BUse an Inception model that is available with the SageMaker image classification algorithm.
  • CCreate a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3. (correct answer)
  • DInitiate transfer learning. Train the model by using the images of less common species. (correct answer)
  • EUse an augmented manifest file in JSON Lines format.
Reveal answer & explanation
Correct answer: C, D

The correct answer is C, D. Option C: Create a.lst file that contains a list of image files and corresponding class labels. Upload the.lst file to Amazon S3. Option D: Initiate transfer learning. Train the model by using the images of less common species.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed.

MLS-C01 Question 253

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Topic 1 · Question 253

A music streaming company is building a pipeline to extract features. The company wants to store the features for offline model training and online inference. The company wants to track feature history and to give the company’s data science teams access to the features. Which solution will meet these requirements with the MOST operational efficiency?

  • AUse Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an IAM role for data scientists to access and search through feature groups. (correct answer)
  • BUse Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an IAM role for data scientists to access and search through feature groups.
  • CCreate one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on versioning for the S3 buckets and use tags to specify which tags are for online inference features and which are for offline model training features. Use Amazon Athena to query the S3 bucket for online inference. Connect the S3 bucket for offline model training to a SageMaker training job. Create an IAM policy that allows data scientists to access both buckets.
  • DCreate two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an IAM policy that allows data scientists to access both tables.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an IAM role for data scientists to...

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. An IAM role grants temporary, least-privilege permissions to AWS resources without long-term credentials. AWS IAM controls authenticated and authorized access to AWS resources with fine-grained policies. This option meets the real-time / low-latency performance requirement.

MLS-C01 Question 254

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Topic 1 · Question 254

A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color. Which solution will meet these requirements with the LEAST amount of effort?

  • AUse an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. (correct answer)
  • BUse an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
  • CUse a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • DUse a semantic segmentation algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Use an object detection algorithm to identify a visitor’s hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

MLS-C01 Question 255

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Topic 1 · Question 255

A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model. Which solution will meet these requirements with the LEAST development effort?

  • AUse SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
  • BUse AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
  • CUse SageMaker Clarify to generate the explanation report. Attach the report to the predicted results. (correct answer)
  • DUse custom Amazon CloudWatch metrics to generate the explanation report. Attach the report to the predicted results.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.

Explanation

Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. This option needs the least custom development effort.

MLS-C01 Question 256

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Topic 1 · Question 256

A financial company sends special offers to customers through weekly email campaigns. A bulk email marketing system takes the list of email addresses as an input and sends the marketing campaign messages in batches. Few customers use the offers from the campaign messages. The company does not want to send irrelevant offers to customers. A machine learning (ML) team at the company is using Amazon SageMaker to build a model to recommend specific offers to each customer based on the customer's profile and the offers that the customer has accepted in the past. Which solution will meet these requirements with the MOST operational efficiency?

  • AUse the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
  • BUse the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker endpoint to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
  • CUse the Neural Collaborative Filtering algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system.
  • DUse the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer recommendations into the bulk email marketing system. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Use the Factorization Machines algorithm to build a model that can generate personalized offer recommendations for customers. Deploy a SageMaker batch inference job to generate offer recommendations. Feed the offer re...

Explanation

AWS Batch schedules and runs batch computing jobs at any scale without managing clusters. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service. Amazon Personalize builds real-time recommendation systems.

MLS-C01 Question 257

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Topic 1 · Question 257

A social media company wants to develop a machine learning (ML) model to detect inappropriate or offensive content in images. The company has collected a large dataset of labeled images and plans to use the built-in Amazon SageMaker image classification algorithm to train the model. The company also intends to use SageMaker pipe mode to speed up the training. The company splits the dataset into training, validation, and testing datasets. The company stores the training and validation images in folders that are named Training and Validation, respectively. The folders contain subfolders that correspond to the names of the dataset classes. The company resizes the images to the same size and generates two input manifest files named training.lst and validation.lst, for the training dataset and the validation dataset, respectively. Finally, the company creates two separate Amazon S3 buckets for uploads of the training dataset and the validation dataset. Which additional data preparation steps should the company take before uploading the files to Amazon S3?

  • AGenerate two Apache Parquet files, training.parquet and validation.parquet, by reading the images into a Pandas data frame and storing the data frame as a Parquet file. Upload the Parquet files to the training S3 bucket.
  • BCompress the training and validation directories by using the Snappy compression library. Upload the manifest and compressed files to the training S3 bucket.
  • CCompress the training and validation directories by using the gzip compression library. Upload the manifest and compressed files to the training S3 bucket.
  • DGenerate two RecordIO files, training.rec and validation.rec, from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordIO files to the training S3 bucket. (correct answer)
Reveal answer & explanation
Correct answer: D

The correct answer is D. Option D: Generate two RecordIO files, training.rec and validation.rec, from the manifest files by using the im2rec Apache MXNet utility tool. Upload the RecordIO files to the training S3 bucket.

Explanation

Amazon S3 provides durable, scalable object storage that is fully managed.

MLS-C01 Question 258

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Topic 1 · Question 258

A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations. Which solution will meet these requirements with LEAST development effort?

  • AUse AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
  • BUse AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.
  • CUse Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details. (correct answer)
  • DUse Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.
Reveal answer & explanation
Correct answer: C

The correct answer is C. Option C: Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

Explanation

Amazon Rekognition adds image and video analysis with pre-trained models. AWS CloudTrail records API activity for auditing and governance. This option needs the least custom development effort.

MLS-C01 Question 259

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Topic 1 · Question 259 · Select all that apply

A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority. A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic. Which algorithms are best suited to this scenario? (Choose two.)

  • ALatent Dirichlet allocation (LDA) (correct answer)
  • BRandom forest classifier
  • CNeural topic modeling (NTM) (correct answer)
  • DLinear support vector machine
  • ELinear regression
Reveal answer & explanation
Correct answer: A, C

The correct answer is A, C. Option A: Latent Dirichlet allocation (LDA) Option C: Neural topic modeling (NTM).

MLS-C01 Question 260

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Topic 1 · Question 260

A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation. Which solution will meet these requirements with the LEAST development effort?

  • AIndex company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions. (correct answer)
  • BTrain a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
  • CTrain an Amazon SageMaker Blazing Text model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
  • DIndex company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.
Reveal answer & explanation
Correct answer: A

The correct answer is A. Option A: Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.

Explanation

Amazon Kendra provides intelligent enterprise search. This option needs the least custom development effort.

Showing questions 241–260 of 369 · Page 13 of 19