A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents?

Create an appropriate pronunciation lexicon.

Convert current documents to SSML with pronunciation tags

Create an appropriate pronunciation lexicon.

Output speech marks to guide in pronunciation

Use Amazon Lex to preprocess the text files for pronunciation

A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)

Install the SageMaker Spark library in the Spark environment.

Use the appropriate estimator from the SageMaker Spark Library to train a model.

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker

Download the AWS SDK for the Spark environment

Install the SageMaker Spark library in the Spark environment.

Use the appropriate estimator from the SageMaker Spark Library to train a model.

Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker

Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

A Machine Learning Specialist is working with a large cybersecurily 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 analysisWhat is the MOST efficient way to accomplish these tasks'?

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

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

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.

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

Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3Which approach will provide the information required for further analysis?

Use Amazon Comprehend with the transcribed files to build the key topics

Use Amazon Translate with the transcribed files to train and build a model for the key topics

Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics

Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

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 unstableWhat should be done to reduce the impact of having such a large number of features?

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

Perform one-hot encoding on highly correlated features

Use matrix multiplication on highly correlated features.

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

Apply the Pearson correlation coefficient

A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year agoWhich method should the Specialist try to improve model performance?

The model should be periodically retrained using the original training data plus new data as product inventory changes

The model needs to be completely re-engineered because it is unable to handle product inventory changes

The model's hyperparameters should be periodically updated to prevent drift

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

The model should be periodically retrained using the original training data plus new data as product inventory changes

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 trainingWhich is the MOST suitable predictive model that can be deployed into production'?

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.

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

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.

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

A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the datasetWhich tool should be used to improve the validation accuracy?

Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Amazon Comprehend syntax analysts and entity detection

Amazon SageMaker BlazingText allow mode

Natural Language Toolkit (NLTK) stemming and stop word removal

Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searchesWhat actions would allow the Specialist to get relevant numerical representations?

Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

Reduce image resolution and use reduced resolution pixel values as features

Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels

Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

Average colors by channel to obtain three-dimensional representations of images.

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 usersThe 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 patternsUsing 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? (Select TWO.)

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

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

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

indicate a copy of the samples in the test database in the training dataset

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

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

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

While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

The model might have prediction errors over a range of target values.

The dataset cannot be accurately represented using the regression model

There are too many variables in the model

The model is predicting its target values perfectly.

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?

The class distribution in the dataset is imbalanced

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the modelWhat should the Specialist do to address the performance issues with the current solution?

Ensure that the input mode for the training job is set to Pipe.

Use the SageMaker batch transform feature

Compress the training data into Apache Parquet format.

Ensure that the input mode for the training job is set to Pipe.

Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variantWhich approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test'?

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker

Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.

An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)

An AWS KMS key policy that allows access to the customer master key (CMK)

A SageMaker notebook security group that allows access to Amazon S3

An 1AM role that allows access to the specific S3 bucket

An AWS KMS key policy that allows access to the customer master key (CMK)

A SageMaker notebook security group that allows access to Amazon S3

An 1AM role that allows access to the specific S3 bucket

An S3 bucket owner that matches the notebook owner

A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the dataWhich type of data repository is the MOST cost-effective solution?

An Amazon S3 data lake with hourly object prefixes

An Amazon EBS-backed Amazon EC2 instance with hourly directories

An Amazon RDS database with hourly table partitions

An Amazon S3 data lake with hourly object prefixes

An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes

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 lo be retrained dailyGiven the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.

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

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

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.

Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.

A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city as this is a prototype, only daily data from the last year is availableWhich model is MOST likely to provide the best results in Amazon SageMaker?

Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.

Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3Which solution takes the LEAST effort to implement?

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

p3.2xlarge (GPU accelerated computing)

p3 8xlarge (GPU accelerated computing)

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 paidWhat steps should be taken to implement a machine learning model to identify potential new customers on social media?

Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

Launch the notebook instances in a public subnet and access the data through the public S3 endpoint

Launch the notebook instances in a private subnet and access the data through a NAT gateway

Launch the notebook instances in a public subnet and access the data through a NAT gateway

Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the trainingWhat should the Specialist do to optimize the data for training on SageMaker'?

Transform the dataset into the Recordio protobuf format

Use the SageMaker batch transform feature to transform the training data into a DataFrame

Use AWS Glue to compress the data into the Apache Parquet format

Transform the dataset into the Recordio protobuf format

Use the SageMaker hyperparameter optimization feature to automatically optimize the data

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.What should the Specialist do to initialize the model to re-train it with the custom data?

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers including the last fully connected layer

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers and replace the last fully connected layer

Initialize the model with pre-trained weights in all layers including the last fully connected layer

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?

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.

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

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.

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 Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions -Here is an example from the dataset'The quck BROWN FOX jumps over the lazy dog 'Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

Normalize all words by making the sentence lowercase

Remove stop words using an English stopword dictionary.

Tokenize the sentence into words.

Perform part-of-speech tagging and keep the action verb and the nouns only

Normalize all words by making the sentence lowercase

Remove stop words using an English stopword dictionary.

Correct the typography on 'quck' to 'quick.'

One-hot encode all words in the sentence

Tokenize the sentence into words.

This graph shows the training and validation loss against the epochs for a neural networkThe network being trained is as follows* Two dense layers one output neuron* 100 neurons in each layer* 100 epochs* Random initialization of weights Which technique can be used to improve model performance in terms of accuracy in the validation set?

Random initialization of weights with appropriate seed

Increasing the number of epochs

Adding another layer with the 100 neurons

A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%What should the Specialist consider to fix this issue1?

Using some form of regularization

Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

Have Amazon ML split the data sequentially.

Pre-split the data before uploading to Amazon S3

Have Amazon ML split the data randomly.

Have Amazon ML split the data sequentially.

Perform custom cross-validation on the data

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.Which solution requires the LEAST coding effort?

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracyWhich method is the MOST repeatable and requires the LEAST amount of effort to achieve this?

Create a hyperparameter tuning job and set the accuracy as an objective metric.

Launch multiple training jobs in parallel with different hyperparameters

Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters

Create a hyperparameter tuning job and set the accuracy as an objective metric.

Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter

IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learningThe ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:* Solution simplicity* Fast development time* Low cost* High flexibilityWhat technologies meet the company's requirements?

Amazon DynamoDB and DynamoDB Accelerator (DAX)

Amazon MLS-C01 Practice Test 3 - Free Online Exam Questions 81-120

A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Amazon MLS-C01 image Question 81 4645 09162024005655000000

Considering the graph, what is a reasonable selection for the optimal choice of k?

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Amazon MLS-C01 Practice Test 3

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