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Exam Details
The Amazon MLS-C01 exam consists of 65 questions that are presented in the multiple-response and multiple-choice formats. These items have to be answered within the allocated time of 170 minutes. It is required that the candidates get 750 points on a scale of 100-1000. This test is available in many languages, including English, Simplified Chinese, Japanese, and Korean. To schedule it, you have to pay the fee of $300.
NEW QUESTION # 94
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.
The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.
Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?
- A. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances.
Store the resulting transcriptions in the transcription S3 bucket. - B. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.
- C. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket
- D. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
Answer: D
Explanation:
Explanation
The company needs to transcribe petabytes of audio files from an on-premises VoIP system to an S3 bucket in the AWS Cloud. The transcription algorithm requires GPUs for inference, which are not available on the on-premises system. The VPN connection over a 100 Mbps connection is not sufficient to transfer the large amount of data quickly. Therefore, the company should use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm locally and leverage the GPU power.
The device can store up to 42 TB of data and can be shipped back to AWS for data ingestion. The company can use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket in the AWS Cloud.
This solution minimizes the network bandwidth and latency issues and enables faster data processing and transfer.
Option B is incorrect because AWS Snowcone is a small, portable, rugged, and secure edge computing and data transfer device that can store up to 8 TB of data. It is not suitable for processing petabytes of data and does not support GPU-based instances.
Option C is incorrect because AWS Outposts is a service that extends AWS infrastructure, services, APIs, and tools to virtually any data center, co-location space, or on-premises facility. It is not designed for data transfer and ingestion, and it would require additional infrastructure and maintenance costs.
Option D is incorrect because AWS DataSync is a service that makes it easy to move large amounts of data to and from AWS over the internet or AWS Direct Connect. However, using DataSync to ingest the audio files to S3 would still be limited by the network bandwidth and latency. Moreover, running the transcription algorithm on AWS Lambda would incur additional costs and complexity, and it would not leverage the GPU power that the algorithm requires.
References:
AWS Snowball Edge Compute Optimized
AWS DataSync
AWS Snowcone
AWS Outposts
AWS Lambda
NEW QUESTION # 95
A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?
- A. Remove the stopwords from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
- B. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
- C. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
- D. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
Answer: A
NEW QUESTION # 96
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?
- A. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- B. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
- C. Write a direct connection to the SQL database within the notebook and pull data in
- D. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
Answer: D
NEW QUESTION # 97
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
- A. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
- C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- D. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
Answer: D
NEW QUESTION # 98
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. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.
- B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
- C. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
- D. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
Answer: A
NEW QUESTION # 99
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. Add more consumers using the Kinesis Client Library (KCL).
- D. Increase the number of shards for the data stream.
Answer: D
NEW QUESTION # 100
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?
- A. Concatenate the features with high correlation scores by using a Jupyter notebook.
- B. Drop the features with low correlation scores by using a Jupyter notebook.
- C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
- D. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
Answer: D
NEW QUESTION # 101
A Data Scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model.
What action will definitively help the model detect more than 10% of fraud cases?
- A. Using undersampling to balance the dataset
- B. Using oversampling to balance the dataset
- C. Decreasing the class probability threshold
- D. Using regularization to reduce overfitting
Answer: C
Explanation:
Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision.
NEW QUESTION # 102
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. Configure Docker Hub and upload the image 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. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
- D. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
Answer: A
NEW QUESTION # 103
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. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
- B. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
- C. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
- D. 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.
Answer: C
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION # 104
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?
- A. AWS Glue with a custom ETL script to transform the data.
- B. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
- C. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
- D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
Answer: C
Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/
NEW QUESTION # 105
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
- A. Bundle the NVIDIA drivers with the Docker image.
- B. Build the Docker container to be NVIDIA-Docker compatible.
- C. Organize the Docker container's file structure to execute on GPU instances.
- D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body
Answer: C
NEW QUESTION # 106
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. K-means
- B. Latent Dirichlet Allocation (LDA)
- C. Recurrent neural network (RNN)
- D. Convolutional neural network (CNN)
Answer: D
Explanation:
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 ...
NEW QUESTION # 107
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 .....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. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.
- C. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket
- D. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
Answer: B
Explanation:
Explanation
The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.
The other solutions are less optimal because they require more development effort and additional services.
Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.
References:
Amazon EventBridge and Amazon SageMaker Pipelines integration
Create a pipeline using a JSON specification
Ingest data into a feature group
NEW QUESTION # 108
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?
- A. Convolutional Neural Network - Quantile Regression (CNN-QR)
- B. Exponential Smoothing (ETS)
- C. Autoregressive Integrated Moving Average (AIRMA)
- D. Prophet
Answer: B
NEW QUESTION # 109
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?
- A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
- B. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
- C. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
- D. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
Answer: B
NEW QUESTION # 110
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?
- A. Use 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.
- B. Use 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.
- C. Use 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.
- D. 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-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
Answer: D
Explanation:
Explanation
The solution C will improve the computational efficiency of the models because it uses Amazon SageMaker Debugger and pruning, which are techniques that can reduce the size and complexity of the convolutional neural network (CNN) models. The solution C involves the following steps:
Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Amazon SageMaker Debugger is a service that can capture and analyze the tensors that are emitted during the training process of machine learning models. Amazon SageMaker Debugger can provide insights into the model performance, quality, and convergence. Amazon SageMaker Debugger can also help to identify and diagnose issues such as overfitting, underfitting, vanishing gradients, and exploding gradients1.
Compute the filter ranks based on the training information. Filter ranking is a technique that can measure the importance of each filter in a convolutional layer based on some criterion, such as the average percentage of zero activations or the L1-norm of the filter weights. Filter ranking can help to identify the filters that have little or no contribution to the model output, and thus can be removed without affecting the model accuracy2.
Apply pruning to remove the low-ranking filters. Pruning is a technique that can reduce the size and complexity of a neural network by removing the redundant or irrelevant parts of the network, such as neurons, connections, or filters. Pruning can help to improve the computational efficiency, memory usage, and inference speed of the model, as well as to prevent overfitting and improve generalization3.
Set the new weights based on the pruned set of filters. After pruning, the model will have a smaller and simpler architecture, with fewer filters in each convolutional layer. The new weights of the model can be set based on the pruned set of filters, either by initializing them randomly or by fine-tuning them from the original weights4.
Run a new training job with the pruned model. The pruned model can be trained again with the same or a different dataset, using the same or a different framework or algorithm. The new training job can use the same or a different configuration of Amazon SageMaker, such as the instance type, the hyperparameters, or the data ingestion mode. The new training job can also use Amazon SageMaker Debugger to monitor and analyze the training process and the model quality5.
The other options are not suitable because:
Option A: Using Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs will not be as effective as using Amazon SageMaker Debugger.
Amazon CloudWatch is a service that can monitor and observe the operational health and performance of AWS resources and applications. Amazon CloudWatch can provide metrics, alarms, dashboards, and logs for various AWS services, including Amazon SageMaker. However, Amazon CloudWatch does not provide the same level of granularity and detail as Amazon SageMaker Debugger for the tensors that are emitted during the training process of machine learning models. Amazon CloudWatch metrics are mainly focused on the resource utilization and the training progress, not on the model performance, quality, and convergence6.
Option B: Using Amazon SageMaker Ground Truth to build and run data labeling workflows and collecting a larger labeled dataset with the labeling workflows will not improve the computational efficiency of the models. Amazon SageMaker Ground Truth is a service that can create high-quality training datasets for machine learning by using human labelers. A larger labeled dataset can help to improve the model accuracy and generalization, but it will not reduce the memory, battery, and hardware consumption of the model. Moreover, a larger labeled dataset may increase the training time and cost of the model7.
Option D: Using Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model and increasing the model learning rate will not improve the computational efficiency of the models. Amazon SageMaker Model Monitor is a service that can monitor and analyze the quality and performance of machine learning models that are deployed on Amazon SageMaker endpoints. The ModelLatency metric and the OverheadLatency metric can measure the inference latency of the model and the endpoint, respectively.
However, these metrics do not provide any information about the training weights, gradients, biases, and activation outputs of the model, which are needed for pruning. Moreover, increasing the model learning rate will not reduce the size and complexity of the model, but it may affect the model convergence and accuracy.
References:
1: Amazon SageMaker Debugger
2: Pruning Convolutional Neural Networks for Resource Efficient Inference
3: Pruning Neural Networks: A Survey
4: Learning both Weights and Connections for Efficient Neural Networks
5: Amazon SageMaker Training Jobs
6: Amazon CloudWatch Metrics for Amazon SageMaker
7: Amazon SageMaker Ground Truth
8: Amazon SageMaker Model Monitor
NEW QUESTION # 111
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A. Area Under the ROC Curve (AUC)
- B. Mean absolute percentage error (MAPE)
- C. Recall
- D. Misclassification rate
Answer: A
Explanation:
Another benefit of using AUC is that it is classification-threshold-invariant like log loss.
https://towardsdatascience.com/the-5-classification-evaluation-metrics-you-must-know- aa97784ff226
NEW QUESTION # 112
A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)
- A. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
- B. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
- C. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
- D. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
- E. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
Answer: B,E
Explanation:
Reference:
https://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf
NEW QUESTION # 113
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?
- A. Dataset shuffling is disabled
- B. The batch size is too big
- C. The class distribution in the dataset is imbalanced
- D. The learning rate is very high
Answer: C
NEW QUESTION # 114
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?
- A. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
- B. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.
- C. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
- D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
Answer: D
Explanation:
Explanation
NEW QUESTION # 115
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
- A. The learning rate should be increased because the optimization process was trapped at a local minimum.
- B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
- C. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
- D. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
Answer: C
Explanation:
Explanation
The best way to address the overfitting problem in image classification is to increase the dropout rate at the flatten layer because the model is not generalized enough. Dropout is a regularization technique that randomly drops out some units from the neural network during training, reducing the co-adaptation of features and preventing overfitting. The flatten layer is the layer that converts the output of the convolutional layers into a one-dimensional vector that can be fed into the dense layers. Increasing the dropout rate at the flatten layer means that more features from the convolutional layers will be ignored, forcing the model to learn more robust and generalizable representations from the remaining features.
The other options are not correct for this scenario because:
Increasing the learning rate would not help with the overfitting problem, as it would make the optimization process more unstable and prone to overshooting the global minimum. A high learning rate can also cause the model to diverge or oscillate around the optimal solution, resulting in poor performance and accuracy.
Increasing the dimensionality of the dense layer next to the flatten layer would not help with the overfitting problem, as it would make the model more complex and increase the number of parameters to be learned. A more complex model can fit the training data better, but it can also memorize the noise and irrelevant details in the data, leading to overfitting and poor generalization.
Increasing the epoch number would not help with the overfitting problem, as it would make the model train longer and more likely to overfit the training data. A high epoch number can cause the model to converge to the global minimum, but it can also cause the model to over-optimize the training data and lose the ability to generalize to new data.
References:
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
How to Reduce Overfitting With Dropout Regularization in Keras
How to Control the Stability of Training Neural Networks With the Learning Rate How to Choose the Number of Hidden Layers and Nodes in a Feedforward Neural Network?
How to decide the optimal number of epochs to train a neural network?
NEW QUESTION # 116
A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.
What is the MOST effective way to encode this categorical feature into a numeric feature?
- A. Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.
- B. Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.
- C. Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.
- D. Fix the spelling in the column by using char-RNN. Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.
Answer: B
Explanation:
Explanation
The most effective way to encode this categorical feature into a numeric feature is to use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings of vectors of real numbers. Similarity encoding is a technique that transforms categorical features into numerical features by computing the similarity between the categories. Similarity encoding can handle high cardinality and redundancy in categorical features, as it can group similar categories together based on their string similarity. For example, if the column contains the values "aspirin", "asprin", and "ibuprofen", similarity encoding will assign a high similarity score to "aspirin" and "asprin", and a low similarity score to "ibuprofen". Similarity encoding can also create embeddings of vectors of real numbers, which can be used as input for machine learning models.
Amazon SageMaker Data Wrangler is a feature of Amazon SageMaker that enables you to prepare data for machine learning quickly and easily. You can use SageMaker Data Wrangler to apply similarity encoding to a column of categorical data, and generate embeddings of vectors of real numbers that capture the similarity between the categories1. The other options are either less effective or more complex to implement. Spell checking the column and using one-hot encoding would require additional steps and resources, and may not capture all the misspellings or redundancies. One-hot encoding would also create a large number of features, which could increase the dimensionality and sparsity of the data. Ordinal encoding would assign an arbitrary order to the categories, which could introduce bias or noise in the data. References:
1: Amazon SageMaker Data Wrangler - Amazon Web Services
NEW QUESTION # 117
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Choose two.)
- A. The principal component analysis (PCA) algorithm
- B. The k-means algorithm
- C. The Latent Dirichlet Allocation (LDA) algorithm
- D. The Random Cut Forest (RCF) algorithm
- E. The factorization machines (FM) algorithm
Answer: A,B
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION # 118
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?
- A. Redeploy the model on a P3dn instance.
- B. Redeploy the model as a batch transform job on an M5 instance.
- C. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
- D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
Answer: C
Explanation:
https://aws.amazon.com/machine-learning/elastic-inference/
NEW QUESTION # 119
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