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Google Professional Machine Learning Engineer Sample Questions (Q57-Q62):

NEW QUESTION # 57
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A. Redeploy the model on a P3dn instance.
• B. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
• C. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• D. Redeploy the model as a batch transform job on an M5 instance.

Answer: B
NEW QUESTION # 58
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

• A. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
• B. An optimization objective that minimizes Log loss
• C. An optimization objective that maximizes the Precision at a Recall value of 0.50
• D. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Answer: A
NEW QUESTION # 59
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

• A. Vertex AI Pipelines and Al Platform Prediction
• B. Cloud Composer, BigQuery ML , and Al Platform Prediction
• C. Vertex AI Pipelines and App Engine
• D. Cloud Composer, Al Platform Training with custom containers , and App Engine

Answer: A
NEW QUESTION # 60
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?

• A. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
• B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
• C. Standardize the data by transforming it with a logarithmic function.
• D. Normalize the data by scaling it to have values between 0 and 1.

Answer: D
NEW QUESTION # 61
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?
A)

B)

C)

D)

• A. Option C
• B. Option D
• C. Option A
• D. Option B

Answer: D Explanation:
If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use
NEW QUESTION # 62
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