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Google Updated Professional-Machine-Learning-Engineer Exam Questions and Answers by cerys

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Google Professional-Machine-Learning-Engineer Exam Overview :

Exam Name: Google Professional Machine Learning Engineer
Exam Code: Professional-Machine-Learning-Engineer Dumps
Vendor: Google Certification: Machine Learning Engineer
Questions: 285 Q&A's Shared By: cerys
Question 20

You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?

Choose 2 answers

Options:

A.

Increase the score threshold.

B.

Decrease the score threshold.

C.

Add more positive examples to the training set.

D.

Add more negative examples to the training set.

E.

Reduce the maximum number of node hours for training.

Discussion
Question 21

You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?

Options:

A.

Configure a Compute Engine instance with Spark and use Jupyter notebooks.

B.

Set up a Dataproc cluster with Spark and use Jupyter notebooks.

C.

Set up a Vertex AI Workbench instance with a Spark kernel.

D.

Use Colab Enterprise with a Spark kernel.

Discussion
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Question 22

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?

Options:

A.

Write your data in TFRecords.

B.

Z-normalize all the numeric features.

C.

Oversample the fraudulent transaction 10 times.

D.

Use one-hot encoding on all categorical features.

Discussion
Question 23

You are an AI architect at a popular photo-sharing social media platform. Your organization’s content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

Options:

A.

Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.

B.

Train an image clustering model using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.

C.

Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy it to a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.

D.

Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.

Discussion
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