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?

A. Listwise deletion

B. Last observation carried forward

C. Multiple imputation

D. Mean substitution

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

A. Poisson distribution

B. Uniform distribution

C. Normal distribution

D. Binomial distribution

A Machine Learning Specialist is attempting to build a linear regression model.
 
Given the displayed residual plot only, what is the MOST likely problem with the model?

A. Linear regression is inappropriate. The residuals do not have constant variance.

B. Linear regression is inappropriate. The underlying data has outliers.

C. Linear regression is appropriate. The residuals have a zero mean.

D. Linear regression is appropriate. The residuals have constant variance.

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

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

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

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

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

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

A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?

A. Gather more data using Amazon Mechanical Turk and then retrain

B. Train an anomaly detection model instead of an MLP

C. Train an XGBoost model instead of an MLP

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

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?

A. Supervised learning

B. Unsupervised learning

C. Semi-supervised learning

D. Reinforcement learning

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 dependent.
Why could this be an issue for the linear least squares regression model?

A. It could cause the backpropagation algorithm to fail during training

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

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

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

A company operates large cranes at a busy port The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.
The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and temperature for each crane. The company contracts AWS ML experts to implement an ML solution.
Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Choose two.)

A. The historical sensor data does not include a significant number of data points and attributes for certain time periods.

B. The historical sensor data shows that simple rule-based thresholds can predict crane failures.

C. The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.

D. The historical sensor data from the cranes are available with high granularity for the last 3 years.

E. The historical sensor data contains most common types of crane failures that the company wants to predict.

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 factorization machines (FM) algorithm

B. The Latent Dirichlet Allocation (LDA) algorithm

C. The principal component analysis (PCA) algorithm

D. The k-means algorithm

E. The Random Cut Forest (RCF) algorithm

An automotive company is using computer vision in its autonomous cars. The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN). The models are trained with PyTorch through the use of the Amazon SageMaker SDK. The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving.
Which solution should the company use to evaluate and improve the performance of the models?

A. Use Amazon CloudWatch algorithm metrics for visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.

B. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.

C. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.

D. Use SageMaker Model Monitor for visibility into the ModelLatency metric and OverheadLatency metric of the model after the model is deployed. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.

A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameters that will generate the most accurate model.
Which approach will meet these requirements with the LEAST operational overhead?

A. Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster. Apply k-fold cross-validation methods to the algorithm.

B. Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.

C. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.

D. Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.

A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?

A. Master node

B. Any of the core nodes

C. Any of the task nodes

D. Both core and task nodes

A data scientist for a medical diagnostic testing company has developed a machine learning (ML) model to identify patients who have a specific disease. The dataset that the scientist used to train the model is imbalanced. The dataset contains a large number of healthy patients and only a small number of patients who have the disease. The model should consider that patients who are incorrectly identified as positive for the disease will increase costs for the company.
Which metric will MOST accurately evaluate the performance of this model?

A. Recall

B. F1 score

C. Accuracy

D. Precision

A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

A. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.

B. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

C. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.

D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

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

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

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

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

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

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression.
During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable.
What should be done to reduce the impact of having such a large number of features?

A. Perform one-hot encoding on highly correlated features.

B. Use matrix multiplication on highly correlated features.

C. Create a new feature space using principal component analysis (PCA)

D. Apply the Pearson correlation coefficient.

A Machine Learning Specialist is working with a large cybersecurity company that manages security events in real time for companies around the world. The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis.
What is the MOST efficient way to accomplish these tasks?

A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.

B. Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k-means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.

C. Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

D. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.

A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.
What should the ML specialist do to improve the model results?

A. Increase the L1 regularization parameter. Do not change any other training parameters.

B. Decrease the L1 regularization parameter. Do not change any other training parameters.

C. Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.

D. Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.

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 only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to Apache Parquet format.

B. Convert the records to JSON format.

C. Convert the records to GZIP CSV format.

D. Convert the records to XML format.

A data scientist is implementing a deep learning neural network model for an object detection task on images. The data scientist wants to experiment with a large number of parallel hyperparameter tuning jobs to find hyperparameters that optimize compute time.
The data scientist must ensure that jobs that underperform are stopped. The data scientist must allocate computational resources to well-performing hyperparameter configurations. The data scientist is using the hyperparameter tuning job to tune the stochastic gradient descent (SGD) learning rate, momentum, epoch, and mini-batch size.
Which technique will meet these requirements with LEAST computational time?

A. Grid search

B. Random search

C. Bayesian optimization

D. Hyperband

A company operates an amusement park. The company wants to collect, monitor, and store real-time traffic data at several park entrances by using strategically placed cameras. The company’s security team must be able to immediately access the data for viewing. Stored data must be indexed and must be accessible to the company’s data science team.
Which solution will meet these requirements MOST cost-effectively?

A. Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in integration with Amazon Rekognition for viewing by the security team.

B. Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.

C. Use Amazon Rekognition Video and the GStreamer plugin to ingest the data for viewing by the security team. Use Amazon Kinesis Data Streams to index and store the data.

D. Use Amazon Kinesis Data Firehose to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.

A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.

B. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.

C. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.

D. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.

A company deployed a machine learning (ML) model on the company website to predict real estate prices. Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.
The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.
Which solution will meet these requirements?

A. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

B. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyperparameters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.

C. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team. Retrain the model by using only data from the previous several months.

D. Use only data from the previous several months to perform incremental training to update the model. Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

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. Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

B. 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.

C. Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings of vectors of real numbers.

D. Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between 0 and the total number of categories in the column.

A data scientist is reviewing customer comments about a company's products. The data scientist needs to present an initial exploratory analysis by using charts and a word cloud. The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model.
Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)

A. Named entity recognition

B. Coreference

C. Stemming

D. Term frequency-inverse document frequency (TF-IDF)

E. Sentiment analysis

A Machine Learning Specialist is developing a custom video recommendation model for an application. The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket. The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

B. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset

C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.

D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

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?

A. Create 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.

B. Create 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.

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.

D. Create 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.

A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.
Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

A. Increase the randomization of training data in the mini-batches used in training

B. Allocate a higher proportion of the overall data to the training dataset

C. Apply L1 or L2 regularization and dropouts to the training

D. Reduce the number of layers and units (or neurons) from the deep learning network

A digital media company wants to build a customer churn prediction model by using tabular data. The model should clearly indicate whether a customer will stop using the company's services. The company wants to clean the data because the data contains some empty fields, duplicate values, and rare values.
Which solution will meet these requirements with the LEAST development effort?

A. Use SageMaker Canvas to automatically clean the data and to prepare a categorical model.

B. Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model.

C. Use SageMaker Canvas automatic data cleaning and preparation tools. Use the built-in SageMaker XGBoost algorithm to train a regression model.

D. Use SageMaker Data Wrangler to clean the data. Use the SageMaker Autopilot to train a regression model

A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products.
Which solution will meet these requirements with the MOST operational efficiency?

A. Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.

B. Tokenize the data and transform the data into tabular data. Train an Amazon SageMaker k-means model to generate the product categories.

C. Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

D. Train an Amazon SageMaker Blazing Text model to generate the product categories.

A Data Engineer needs to build a model using a dataset containing customer credit card information
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

B. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

C. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

A. Root Mean Square Error (RMSE)

B. Residual plots

C. Area under the curve

D. Confusion matrix

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

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

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

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

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

A company decides to use Amazon SageMaker to develop machine learning (ML) models. The company will host SageMaker notebook instances in a VPC. The company stores training data in an Amazon S3 bucket. Company security policy states that SageMaker notebook instances must not have internet connectivity.
Which solution will meet the company’s security requirements?

A. Connect the SageMaker notebook instances that are in the VPC by using AWS Site-to-Site VPN to encrypt all internet-bound traffic. Configure VPC flow logs. Monitor all network traffic to detect and prevent any malicious activity.

B. Configure the VPC that contains the SageMaker notebook instances to use VPC interface endpoints to establish connections for training and hosting. Modify any existing security groups that are associated with the VPC interface endpoint to allow only outbound connections for training and hosting.

C. Create an IAM policy that prevents access the internet. Apply the IAM policy to an IAM role. Assign the IAM role to the SageMaker notebook instances in addition to any IAM roles that are already assigned to the instances.

D. Create VPC security groups to prevent all incoming and outgoing traffic. Assign the security groups to the SageMaker notebook instances.

A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones
(negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

A. Specificity

B. False positive rate

C. Accuracy

D. Area under the precision-recall curve

E. True positive rate

A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model’s performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.
Which preprocessing step will meet these requirements?

A. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data.

B. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

C. Reduce the dimensionality of the dataset by removing the features that have the highest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Standard Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

D. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.
The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.
Which solution will meet these requirements with the LEAST development effort?

A. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.

B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

C. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

D. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.

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

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

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

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

D. Use k-means clustering to handle missing features

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

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

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

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

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

A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes. The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes.
Which function will produce the desired output?

A. Dropout

B. Smooth L1 loss

C. Softmax

D. Rectified linear units (ReLU)

A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute
(RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training.
Which is the MOST suitable predictive model that can be deployed into production?

A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.

B. This data requires an unsupervised learning algorithm. Use Amazon SageMaker k-means to cluster the data.

C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.

D. This data is already formulated as a time series. Use Amazon SageMaker seq2seq to model the time series.

A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.
A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.
Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?

A. Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.

B. Use the SageMaker BlazingText algorithm.

C. Use the SageMaker Neural Topic Model (NTM) algorithm.

D. Use the SageMaker CatBoost algorithm.

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. The training channel identifying the location of training data on an Amazon S3 bucket.

B. The validation channel identifying the location of validation data on an Amazon S3 bucket.

C. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

D. Hyperparameters in a JSON array as documented for the algorithm used.

E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and 999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory
Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

A. Add more deep trees to the random forest to enable the model to learn more features.

B. Include a copy of the samples in the test dataset in the training dataset.

C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

D. Change the cost function so that false negatives have a higher impact on the cost value than false positives.

E. Change the cost function so that false positives have a higher impact on the cost value than false negatives.

A company hosts a machine learning (ML) dataset repository on Amazon S3. A data scientist is preparing the repository to train a model. The data scientist needs to redact personally identifiable information (PH) from the dataset.
Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon SageMaker Data Wrangler with a custom transformation to identify and redact the PII.

B. Create a custom AWS Lambda function to read the files, identify the PII. and redact the PII

C. Use AWS Glue DataBrew to identity and redact the PII

D. Use an AWS Glue development endpoint to implement the PII redaction from within a notebook

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

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

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

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

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

A machine learning (ML) engineer at a bank is building a data ingestion solution to provide transaction features to financial ML models. Raw transactional data is available in an Amazon Kinesis data stream.
The solution must compute rolling averages of the ingested data from the data stream and must store the results in Amazon SageMaker Feature Store. The solution also must serve the results to the models in near real time.
Which solution will meet these requirements?

A. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to aggregate the data and to load the results into SageMaker Feature Store as an online feature group.

B. Write the data directly from the data stream into SageMaker Feature Store as an online feature group. Calculate the rolling averages in place within SageMaker Feature Store by using the SageMaker GetRecord API operation.

C. Consume the data stream by using an Amazon Kinesis Data Analytics SQL application that calculates the rolling averages. Generate a result stream. Consume the result stream by using a custom AWS Lambda function that publishes the results to SageMaker Feature Store as an online feature group.

D. Load the data into an Amazon S3 bucket by using Amazon Kinesis Data Firehose. Use a SageMaker Processing job to load the data into SageMaker Feature Store as an offline feature group. Compute the rolling averages at query time.

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

A. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.

B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.

C. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.

D. Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.

A company is building an application that can predict spam email messages based on email text. The company can generate a few thousand human-labeled datasets that contain a list of email messages and a label of "spam" or "not spam" for each email message. A machine learning (ML) specialist wants to use transfer learning with a Bidirectional Encoder Representations from Transformers (BERT) model that is trained on English Wikipedia text data.
What should the ML specialist do to initialize the model to fine-tune the model with the custom data?

A. Initialize the model with pretrained weights in all layers except the last fully connected layer.

B. Initialize the model with pretrained weights in all layers. Stack a classifier on top of the first output position. Train the classifier with the labeled data.

C. Initialize the model with random weights in all layers. Replace the last fully connected layer with a classifier. Train the classifier with the labeled data.

D. Initialize the model with pretrained weights in all layers. Replace the last fully connected layer with a classifier. Train the classifier with the labeled data.

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10,000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.
How should the company prepare the data for the model to improve the model's accuracy?

A. Adjust the class weight to account for each machine type.

B. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).

C. Undersample the non-failure events. Stratify the non-failure events by machine type.

D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).