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

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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: ayrton
Question 52

You are going to train a DNN regression model with Keras APIs using this code:

Questions 52

How many trainable weights does your model have? (The arithmetic below is correct.)

Options:

A.

501*256+257*128+2 = 161154

B.

500*256+256*128+128*2 = 161024

C.

501*256+257*128+128*2=161408

D.

500*256*0 25+256*128*0 25+128*2 = 40448

Discussion
Question 53

You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

Options:

A.

Enable VPC Service Controls for peering’s, and add Vertex Al to a service perimeter

B.

Create a Cloud Run endpoint as a proxy to the data Use Identity and Access Management (1AM)

authentication to secure access to the endpoint from the training job.

C.

Configure VPC Peering with Vertex Al and specify the network of the training job

D.

Download the data to a Cloud Storage bucket before calling the training job

Discussion
Question 54

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model. What should you do?

Options:

A.

Standardize the data by transforming it with a logarithmic function.

B.

Apply a principal component analysis (PCA) to minimize the effect of any particular feature.

C.

Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.

D.

Normalize the data by scaling it to have values between 0 and 1.

Discussion
Question 55

You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

Options:

A.

Normalize the data for the training, and test datasets as two separate steps.

B.

Split the training and test data based on time rather than a random split to avoid leakage

C.

Add more data to your test set to ensure that you have a fair distribution and sample for testing

D.

Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

Discussion
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