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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Identifying data sources
• Define ML problem
• Defining problem type (classification, regression, clustering, etc.)
• Assessing data readiness
• Identifying nonML solutions
• Key results
• Defining output use
• Identify risks to feasibility and implementation of ML solution. Considerations include:
• Defining business problems
• Success metrics
• Defining the input (features) and predicted output format
• Aligning with Google AI principles and practices (e.g. different biases)
• Managing incorrect results
• Define business success criteria
• Defining outcome of model predictions
• Assessing and communicating business impact
• Assessing ML solution readiness

Professional Machine Learning Engineer - Google Certified salary

The estimated average salary of Professional Machine Learning Engineer - Google is listed below:

• United States: 114,000 USD
• Europe: 97,000 EURO
• India: 8,580,000 INR
• England: 87,200 POUND

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Google Professional Machine Learning Engineer Sample Questions (Q101-Q106):

NEW QUESTION # 101
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

• A. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
• B. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
• C. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
• D. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.

Answer: B Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION # 102
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

• A. User engagement as measured by the number of battles played daily per user
• B. Average time players wait before being assigned to a team
• C. Rate of return as measured by additional revenue generated minus the cost of developing a new model
• D. Precision and recall of assigning players to teams based on their predicted versus actual ability

Answer: A
NEW QUESTION # 103
You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

• A. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
• B. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
• C. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
• D. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

Answer: B Explanation:
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overviewoflabels You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources.
NEW QUESTION # 104
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

• A. Modify the 'learning rate' parameter
• B. Modify the batch size' parameter
• C. Modify the 'epochs' parameter
• D. Modify the 'scale-tier' parameter

Answer: B
NEW QUESTION # 105
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 to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

• A. 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.
• B. 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.
• C. 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.
• D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.

Answer: D
NEW QUESTION # 106
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