AWS-Certified-Machine-Learning-Specialty Exam - AWS Certified Machine Learning - Specialty

NEW QUESTION 1
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet. How can these requirements be satisfied?

• A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
• B. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
• C. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
• D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

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 data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

• A. Shuffle all interaction dat
• B. Split off the last 10% of the interaction data for the test set.
• C. Identify the most recent 10% of interactions for each use
• D. Split off these interactions for the test set.
• E. Identify the 10% of users with the least interaction dat
• F. Split off all interaction data from these users for the test set.
• G. Randomly select 10% of the user
• H. Split off all interaction data from these users for the test set.

Answer: B

Explanation:
https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagem

NEW QUESTION 4
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions
What metric is BEST suited to score the model?

• A. Precision
• B. Recall
• C. Area Under the ROC Curve (AUC)
• D. Root Mean Square Error (RMSE)

Answer: A

NEW QUESTION 5
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 999.1 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? (Select TWO.)

• A. Add more deep trees to the random forest to enable the model to learn more features.
• B. indicate a copy of the samples in the test database 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

Answer: CD

NEW QUESTION 6
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 dataset
Which tool should be used to improve the validation accuracy?

• A. Amazon Comprehend syntax analysts and entity detection
• B. Amazon SageMaker BlazingText allow mode
• C. Natural Language Toolkit (NLTK) stemming and stop word removal
• D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Answer: A

NEW QUESTION 7
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 8
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

• A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.Perform a manual review on those words before performing a business validation.
• B. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
• C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (AmazonA2I). Perform a manual review on those words before performing a business validation.
• D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images.Use this information to process the loan applications.

Answer: C

NEW QUESTION 9
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?

• A. m5 4xlarge (general purpose)
• B. r5.2xlarge (memory optimized)
• C. p3.2xlarge (GPU accelerated computing)
• D. p3 8xlarge (GPU accelerated computing)

Answer: C

NEW QUESTION 10
A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings?

• A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance ha
• B. Stop the notebook when it is not in us
• C. Run both data preprocessing and feature engineering development on that instance.
• D. Keep the notebook instance type and size the sam
• E. Stop the notebook when it is not in us
• F. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• G. Change the notebook instance type to a smaller general purpose instanc
• H. Stop the notebook when it is not in us
• I. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• J. Change the notebook instance type to a smaller general purpose instanc
• K. Stop the notebook when it is not in us
• L. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

Answer: B

NEW QUESTION 11
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?

• A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
• B. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
• C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
• D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Answer: B

NEW QUESTION 12
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

• A. Decrease regularization.
• B. Increase regularization.
• C. Increase dropout.
• D. Decrease dropout.
• E. Increase feature combinations.
• F. Decrease feature combinations.

Answer: BCD

NEW QUESTION 13
A large consumer goods manufacturer has the following products on sale
• 34 different toothpaste variants
• 48 different toothbrush variants
• 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched
Which solution should a Machine Learning Specialist apply?

• A. Train a custom ARIMA model to forecast demand for the new product.
• B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• D. Train a custom XGBoost model to forecast demand for the new product

Answer: B

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

NEW QUESTION 14
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 ago
Which method should the Specialist try to improve model performance?

• A. The model needs to be completely re-engineered because it is unable to handle product inventory changes
• B. The model's hyperparameters should be periodically updated to prevent drift
• C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
• D. The model should be periodically retrained using the original training data plus new data as product inventory changes

Answer: D

NEW QUESTION 15
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing
The Data Scientist has been given the following requirements for the cloud solution
* Combine multiple data sources
* Reuse existing PySpark logic
* Run the solution on the existing schedule
* Minimize the number of servers that will need to be managed
Which architecture should the Data Scientist use to build this solution?

• A. Write the raw data to Amazon S3 Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule Use the existing PySpark logic to run the ETL job on the EMR cluster Output the results to a "processed" location m Amazon S3 that is accessible tor downstream use
• B. Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
• C. Write the raw data to Amazon S3 Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3 Write the Lambda logic in Python and implement the existing PySpartc logic to perform the ETL process Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use
• D. Use Amazon Kinesis Data Analytics to stream the input data and perform realtime SQL queries against the stream to carry out the required transformations within the stream Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use

Answer: A

NEW QUESTION 16
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data
Which of the following services can feed data to the MapReduce jobs? (Select TWO )

• A. AWSDMS
• B. Amazon Kinesis
• C. AWS Data Pipeline
• D. Amazon Athena
• E. Amazon ES

Answer: BC

Explanation:
https://aws.amazon.com/jp/emr/?whats-new-cards.sort-by=item.additionalFields.postDateTime&whats-new-car

NEW QUESTION 17
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