While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

Data augmentation for the minority class

An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

Last observation carried forward

A Machine Learning Specialist discover the following statistics while experimenting on a model. What can the Specialist from the experiments?

The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1

The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

The model in Experiment 1 had a high random noise error that was reduced in Experiment 3 by regularization Experiment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning modelsDuring the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test imagesWhich of the following should be used to resolve this issue? (Select TWO)

Perform data augmentation on the training data

Add L2 regularization to the model

Add vanishing gradient to the model

Perform data augmentation on the training data

Make the neural network architecture complex.

Use gradient checking in the model

Add L2 regularization to the model

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

Convolutional neural network (CNN)

Latent Dirichlet Allocation (LDA)

Convolutional neural network (CNN)

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hoursWith the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s)Which visualization will accomplish this?

A scatter plot showing the correlation between maximum tree depth and the objective metric.

A histogram showing whether the most important input feature is Gaussian.

A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.

A scatter plot showing (he performance of the objective metric over each training iteration

A scatter plot showing the correlation between maximum tree depth and the objective metric.

A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:* Must be accessible from a VPC only.* Must not traverse the public internet.How can these requirements be satisfied?

Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.What combination of services is the MOST efficient to accomplish the task?

Amazon Transcribe, Amazon Translate, and Amazon Comprehend

Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

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?

Build the Docker container to be NVIDIA-Docker compatible.

Bundle the NVIDIA drivers with the Docker image.

Build the Docker container to be NVIDIA-Docker compatible.

Organize the Docker container's file structure to execute on GPU instances.

Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body

A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

Receiver operating characteristic (ROC) curve

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.What should the Specialist do to meet these requirements?

Download word embedding's pre-trained on a large corpus.

Create one-hot word encoding vectors.

Produce a set of synonyms for every word using Amazon Mechanical Turk.

Create word embedding factors that store edit distance with every other word.

Download word embedding's pre-trained on a large corpus.

A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.How should the Data Science team configure the notebook instance placement to meet these requirements?

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.

Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker

A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.Which solution should the Data Scientist build to satisfy the requirements?

Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.

Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

Create a NAT gateway within the corporate VPC.

Route Amazon SageMaker traffic through an on-premises network.

Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

Create VPC peering with Amazon VPC hosting Amazon SageMaker.

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.Which solution should the agency consider?

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.* Support event-driven ETL pipelines.* Provide a quick and easy way to understand metadata.Which approach meets trfese requirements?

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changesWhat should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.

Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.

Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:Total number of images available = 1,000 Test set images = 100 (constant test set)The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.Which techniques can be used by the ML Specialist to improve this specific test error?

Increase the training data by adding variation in rotation for training images.

Increase the number of epochs for model training.

Increase the number of layers for the neural network.

Increase the dropout rate for the second-to-last layer.

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives. Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.Which approach should the Specialist use to continue working?

Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Install Python 3 and boto3 on their laptop and continue the code development using that environment.

Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

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?

Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.

AWS Glue with a custom ETL script to transform the data.

An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns onlyHow should the Machine Learning Specialist transform the dataset to minimize query runtime?

Convert the records to Apache Parquet format

Convert the records to JSON format

Convert the records to GZIP CSV format

Convert the records to XML format

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

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

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

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

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

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 citizenWhich combination of algorithms would provide the appropriate insights? (Select TWO )

The principal component analysis (PCA) algorithm

The factorization machines (FM) algorithm

The Latent Dirichlet Allocation (LDA) algorithm

The principal component analysis (PCA) algorithm

The Random Cut Forest (RCF) algorithm

A large consumer goods manufacturer has the following products on sale* 34 different toothpaste variants* 48 different toothbrush variants* 43 different mouthwash variantsThe entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launchedWhich solution should a Machine Learning Specialist apply?

Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Train a custom ARIMA model to forecast demand for the new product.

Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

Train a custom XGBoost model to forecast demand for the new product

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processingThe Data Scientist has been given the following requirements for the cloud solution* Combine multiple data sources* Reuse existing PySpark logic* Run the solution on the existing schedule* Minimize the number of servers that will need to be managedWhich architecture should the Data Scientist use to build this solution?

Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output target of the ETL job to write to a 'processed' location in Amazon S3 that is accessible for downstream use.

Write the raw data to Amazon S3 Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule Use the existing PySpark logic to run the ETL job on the EMR cluster Output the results to a 'processed' location m Amazon S3 that is accessible tor downstream use

Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output target of the ETL job to write to a 'processed' location in Amazon S3 that is accessible for downstream use.

Write the raw data to Amazon S3 Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3 Write the Lambda logic in Python and implement the existing PySpartc logic to perform the ETL process Have the Lambda function output the results to a 'processed' location in Amazon S3 that is accessible for downstream use

Use Amazon Kinesis Data Analytics to stream the input data and perform realtime SQL queries against the stream to carry out the required transformations within the stream Deliver the output results to a 'processed' location in Amazon S3 that is accessible for downstream use

A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependentWhy could this be an issue for the linear least squares regression model?

It could create a singular matrix during optimization which fails to define a unique solution

It could cause the backpropagation algorithm to fail during training

It could create a singular matrix during optimization which fails to define a unique solution

It could modify the loss function during optimization causing it to fail during training

It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model

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?

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.

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

onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.

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.

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

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

Add L1 regularization to the classifier

Perform recursive feature elimination

Add L1 regularization to the classifier

Perform recursive feature elimination

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

Perform linear discriminant analysis

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.What approach would be the MOST effective to perform near-real time defect detection?

Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.

Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.

Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.

Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.Which approach will provide the MAXIMUM performance boost?

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.

Reduce the learning rate and run the training process until the training loss stops decreasing.

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

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?

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

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.

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

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

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.Which storage option provides the most processing flexibility and will allow access control with IAM?

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.

Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

Amazon MLS-C01 Practice Test 4 - Free Online Exam Questions 121-160

A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions

What metric is BEST suited to score the model?

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Amazon MLS-C01 Practice Test 4

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Amazon MLS-C01 Practice Tests

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