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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)
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?
A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials. ...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%. ...should the company retrain the model to meet these requirements?
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?
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 learning The 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 flexibility What technologies meet the company's requirements?
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 S3 The 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 S3 Which approach will provide the information required for further analysis?
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application. The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
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 data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows: ...traction Timestamp (Timeslamp) ...JName(Varchar) ...JNo (Varchar) Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard. The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster Which solution meets these requirements?
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome. The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years. The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance. Which solution will meet these requirements?

Question 10 - MLS-C01 discussion

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A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.

Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A.
Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
Answers
A.
Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
B.
Use SCPs to restrict access to SageMaker.
Answers
B.
Use SCPs to restrict access to SageMaker.
C.
Disable root access on the SageMaker notebook instances.
Answers
C.
Disable root access on the SageMaker notebook instances.
D.
Enable network isolation for training jobs and models.
Answers
D.
Enable network isolation for training jobs and models.
E.
Restrict notebook presigned URLs to specific IPs used by the company.
Answers
E.
Restrict notebook presigned URLs to specific IPs used by the company.
F.
Protect data with encryption at rest and in transit.
Answers
F.
Protect data with encryption at rest and in transit.
Suggested answer: A, D, F

Explanation:

Use AWS Key Management Service (AWS KMS) to manage encryption keys. To control data egress from SageMaker, the ML engineer can use the following mechanisms: Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet.This reduces the risk of data leakage and unauthorized access1 Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services.This ensures that the data used for training and inference is not exposed to external sources2 Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services.This helps protect the data from unauthorized access and tampering3 The other options are not effective in controlling data egress from SageMaker: Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations.They do not control the data egress from SageMaker, but rather the access to SageMaker itself4 Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances. Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances. References: 1:Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker 2:Network Isolation - Amazon SageMaker 3:Encrypt Data at Rest and in Transit - Amazon SageMaker 4: Using Service Control Policies - AWS Organizations : Disable Root Access - Amazon SageMaker : Create a Presigned Notebook Instance URL - Amazon SageMaker

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asked 16/09/2024
Katherine Messick
37 questions
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