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Google Professional Machine Learning Engineer Sample Questions (Q128-Q133):

NEW QUESTION # 128
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A. Three individual features binned latitude, binned longitude, and one-hot encoded car type
B. One feature obtained as an element-wise product between latitude, longitude, and car type
C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

Answer: A

NEW QUESTION # 129
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A. 1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API
B. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
C. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
D. 1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API

Answer: D

Explanation:
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
-The Cloud Function calls 3 different endpoints to enrich the ticket:
-An AI Platform endpoint, where the function can predict the priority.
-An AI Platform endpoint, where the function can predict the resolution time.
-The Natural Language API to do sentiment analysis and word salience.
-For each reply, the Cloud Function updates the Firebase real-time database.
-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

NEW QUESTION # 130
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

A. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
B. Schedule a weekly query in BigQuery to compute the success metric.
C. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
D. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.

Answer: B

Explanation:
Scheduling a weekly query in BigQuery to compute the success metric is a cost-effective way to monitor the model's performance. BigQuery allows you to run complex queries on large datasets in a cost-effective and performant manner. By using BigQuery, you can compute the success metric on a regular basis without incurring the additional costs of other services such as Vertex AI or Cloud Composer.
Additionally, by scheduling the query to run weekly, you can ensure that you are monitoring the model's performance in a timely manner, while still providing enough time for the model to degrade below the acceptable baseline. You can then use the results of the query to determine when retraining is necessary.

NEW QUESTION # 131
You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

A. Address data leakage by applying nested cross-validation during model training.
B. Address data leakage by removing features highly correlated with the target value.
C. Address the model overfitting by using a less complex algorithm.
D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Answer: A

Explanation:
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9

NEW QUESTION # 132
You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

A. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
B. Use Dataflow to create all possible augmentations, and store them as TFRecords.
C. Embed the augmentation functions dynamically in the tf.Data pipeline.
D. Embed the augmentation functions dynamically as part of Keras generators.

Answer: B

NEW QUESTION # 133
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