The Secret Of Amazon AWS-Certified-Machine-Learning-Specialty Exam Price

NEW QUESTION 1
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following: 68% accuracy on the training set 67% accuracy on the validation set
What can the machine learning specialist do to improve the system’s accuracy?

• A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model’s hyperparameters.
• B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
• C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
• D. Train a new model using the current neural network architecture.

Answer: B

NEW QUESTION 2
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company’s business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?

• A. Build and host multiple models in Amazon SageMake
• B. Create multiple Amazon SageMaker endpoints, one for each mode
• C. Programmatically control invoking different models for inference at the applicationlayer.
• D. Build and host multiple models in Amazon SageMake
• E. Create an Amazon SageMaker endpoint configuration with multiple production variant
• F. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
• G. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical device
• H. Programmatically control which model is invoked for inference based on the medical device type.
• I. Build and host multiple models in Amazon SageMake
• J. Create a single endpoint that accesses multiple model
• K. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

Answer: B

Explanation:
A/B testing with Amazon SageMaker is required in the Exam. In A/B testing, you test different variants of your models and compare how each variant performs. Amazon SageMaker enables you to test multiple models or model versions behind the `same endpoint` using `production variants`. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by `distributing traffic` between them, specify the `percentage of the traffic` that gets routed to each model by specifying the `weight` for each `production variant` in the endpoint configuration.
https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant

NEW QUESTION 3
A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker
When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?
Choose the maximum number of hyperparameters supported by

• A. Amazon SageMaker to search the largest number of combinations possible
• B. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
• C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
• D. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

Answer: C

NEW QUESTION 4
A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average.
The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped.
The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit.
How can the company resolve this issue MOST cost-effectively?

• A. Set up a 10 Gbps AWS Direct Connect connection between the production site and the nearest AWS Regio
• B. Use the Direct Connect connection to upload the image
• C. Increase the size of the instances and the number of instances that are used by the SageMaker endpoint.
• D. Extend the long-running Lambda function that runs on AWS IoT Greengrass to compress the images and upload the compressed files to Amazon S3. Decompress the files by using a separate Lambda function that invokes the existing Lambda function to run the inference pipeline.
• E. Use auto scaling for SageMake
• F. Set up an AWS Direct Connect connection between the production site and the nearest AWS Regio
• G. Use the Direct Connect connection to upload the images.
• H. Deploy the Lambda function and the ML models onto the AWS IoT Greengrass core that is running on the industrial PCs that are installed on each machin
• I. Extend the long-running Lambda function that runs on AWS IoT Greengrass to invoke the Lambda function with the captured images and run the inference on the edge component that forwards the results directly to the web service.

Answer: D

NEW QUESTION 5
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 searches
What actions would allow the Specialist to get relevant numerical representations?

• A. Reduce image resolution and use reduced resolution pixel values as features
• B. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
• C. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
• D. Average colors by channel to obtain three-dimensional representations of images.

Answer: A

NEW QUESTION 6
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A. Redeploy the model as a batch transform job on an M5 instance.
• B. Redeploy the model on an M5 instanc
• C. Attach Amazon Elastic Inference to the instance.
• D. Redeploy the model on a P3dn instance.
• E. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: B

Explanation:
https://aws.amazon.com/machine-learning/elastic-inference/

NEW QUESTION 7
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? (Select 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

Answer: CD

Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.

NEW QUESTION 8
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet this requirements?

• A. Call the CreateNotebookInstanceLifecycleConfig API operation
• B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
• C. Stop and then restart the SageMaker notebook instance
• D. Call the UpdateNotebookInstanceLifecycleConfig API operation

Answer: C

NEW QUESTION 9
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.

Answer: C

Explanation:
If this is a ComputerVision problem augmentation can help and we may consider A an option. However in analyzing customer historic data, there is no easy way to increase randomization in training. If you go deep into modelling and coding. When you build model with tensorflow/pytorch, most of the time the trainloader is already sampling in data in random manner (with shuffle enable). What we usually do to reduce overfitting is by adding dropout.
https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

NEW QUESTION 10
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

• A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
• B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims ineach category to expect from month to month.
• C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect frommonth to month.
• D. Classification with supervised learning of the categories for which partial information on claim contents isprovided, and forecasting using claim IDs and timestamps for all other categories.

Answer: C

NEW QUESTION 11
A retail company uses a machine learning (ML) model for daily sales forecasting. The company’s brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company’s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

• A. Create a histogram of the daily sales over the last 3 week
• B. In addition, create a histogram of the daily sales from before that period.
• C. Create a histogram of the model errors over the last 3 week
• D. In addition, create a histogram of the model errors from before that period.
• E. Create a line chart with the weekly mean absolute error (MAE) of the model.
• F. Create a scatter plot of daily sales versus model error for the last 3 week
• G. In addition, create a scatter plot of daily sales versus model error from before that period.

Answer: C

NEW QUESTION 12
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

• A. Linear regression
• B. Classification
• C. Clustering
• D. Reinforcement learning

Answer: B

Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) – answers that need to be predicted – to train an algorithm. With classification, businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?

NEW QUESTION 13
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

Answer: A

NEW QUESTION 14
A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

• A. Apply integer transformation and set Red = 1, White = 5, and Green = 10.
• B. Add new columns that store one-hot representation of colors.
• C. Replace the color name string by its length.
• D. Create three columns to encode the color in RGB format.
• E. Replace each color name by its training set frequency.

Answer: AD

NEW QUESTION 15
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

• A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
• C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer: B

Explanation:
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

NEW QUESTION 16
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?

• A. 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.
• B. 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.
• C. 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.
• D. 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.

Answer: A

NEW QUESTION 17
The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand The team has a set of training data
Which machine learning algorithm should the researchers use that BEST meets their requirements?

• A. Latent Dirichlet Allocation (LDA)
• B. Recurrent neural network (RNN)
• C. K-means
• D. Convolutional neural network (CNN)

Answer: C

NEW QUESTION 18
A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made.
The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days.
The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science.
What should the data science team do to address this issue in the MOST operationally efficient manner?

• A. Use Amazon SageMaker Studio to rebuild the mode
• B. Create a notebook that uses the XGBoost training container to perform model trainin
• C. Deploy the model at an endpoin
• D. Enable Amazon SageMaker Model Monitor to store inference
• E. Use the inferences to create Shapley values that help explain model behavio
• F. Create a chart that shows features and SHapley Additive explanation (SHAP) values to explain to the credit team how the features affect the model outcomes.
• G. Use Amazon SageMaker Studio to rebuild the mode
• H. Create a notebook that uses the XGBoost training container to perform model trainin
• I. Activate Amazon SageMaker Debugger, and configure it to calculate and collect Shapley value
• J. Create a chart that shows features and SHapley Additive explanation (SHAP) values to explain to the credit team how the features affect the model outcomes.
• K. Create an Amazon SageMaker notebook instanc
• L. Use the notebook instance and the XGBoost library to locally retrain the mode
• M. Use the plot_importance() method in the Python XGBoost interface to create a feature importance char
• N. Use that chart to explain to the credit team how the features affect the model outcomes.
• O. Use Amazon SageMaker Studio to rebuild the mode
• P. Create a notebook that uses the XGBoost training container to perform model trainin
• Q. Deploy the model at an endpoin
• R. Use Amazon SageMakerProcessing to post-analyze the model and create a feature importance explainability chart automatically for the credit team.

Answer: C

NEW QUESTION 19
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 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 EM
• C. 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
• D. 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.
• E. 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

Answer: A

NEW QUESTION 20
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