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Professional Machine Learning Engineer - Google Certified salary

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

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

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

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

• Modeling techniques given interpretability requirements
• Model generalization
• Build a model
• Model performance against baselines, simpler models, and across the time dimension
• Distributed training
• Tracking metrics during training
• Productionizing
• Transfer learning
• Training a model as a job in different environments
• Scale model training and serving
• Model explainability on Cloud AI Platform

Google Professional Machine Learning Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

• A. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
• B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
• C. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
• D. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Answer: B

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html

NEW QUESTION # 36
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

• A. Streaming classification
• B. Binary classification
• C. Multi-category classification
• D. Regression classification

Answer: C

NEW QUESTION # 37
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

• A. Poor data quality
• B. Too few layers in the model for capturing information
• C. Incorrect data split ratio during model training, evaluation, validation, and test
• D. Lack of model retraining

Answer: D

Explanation:
Retraining is needed as the market is changing. its how the Model keep updated and predictions accuracy.

NEW QUESTION # 38
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?

• A. Z-normalize all the numeric features.
• B. Oversample the fraudulent transaction 10 times.
• C. Use one-hot encoding on all categorical features.
• D. Write your data in TFRecords.

Answer: B

NEW QUESTION # 39
You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?

• A. 1. Export your data to Cloud Storage using Dataflow.
  2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
  3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
• B. 1. Export the data to Cloud Storage using the BigQuery command-line tool
  2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
  3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
• C. 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
  2. Your application queries a Vertex AI endpoint where you deployed your model.
  3. Responses are received by the caller application as soon as the model produces the prediction.
• D. 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
  2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
  3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

Answer: A

NEW QUESTION # 40
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