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Google Professional Machine Learning Engineer certification is a highly respected and sought-after certification in the field of machine learning. Google Professional Machine Learning Engineer certification is designed to validate the skills and expertise of professionals who are responsible for designing, building, managing, and deploying machine learning models at scale using Google Cloud technologies. Google Professional Machine Learning Engineer certification exam covers a wide range of topics related to machine learning, and candidates must have a minimum of three years of experience in the field of machine learning to be eligible for the exam.

Google Professional Machine Learning Engineer Sample Questions (Q55-Q60):

NEW QUESTION # 55
Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

A. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.
B. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.
C. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.
D. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.

Answer: C

NEW QUESTION # 56
You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

A. Use the BigQuery API Connector and Cloud Scheduler to trigger. Workflows every week that retrains the model.
B. Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
C. Use BigQuerys scheduling service to run the model retraining query periodically.
D. Create a pipeline in Vertex Al Pipelines that executes the retraining query and use the Cloud Scheduler API to run the query weekly.

Answer: D

Explanation:
BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data.
BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to perform linear regression on your data and create a model. BigQuery also provides a scheduling service that allows you to create and manage recurring SQL queries. You can use BigQuery's scheduling service to run the model retraining query periodically, such as every week. You can specify the destination table for the query results, and the schedule options, such as start date, end date, frequency, and time zone. You can also monitor the status and history of your scheduled queries. This solution can help you retrain the model on the cumulative data collected every week, while minimizing the development effort and the scheduling cost. References:
* BigQuery ML | Google Cloud
* Scheduling queries | BigQuery

NEW QUESTION # 57
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

A. Categorical cross-entropy
B. Categorical hinge
C. Binary cross-entropy
D. Sparse categorical cross-entropy

Answer: A

Explanation:
Categorical cross-entropy is a loss function that is suitable for multi-class classification problems, where the target variable has more than two possible values. Categorical cross-entropy measures thedifference between the true probability distribution of the target classes and the predicted probability distribution of the model. It is defined as:
L = - sum(y_i * log(p_i))
where y_i is the true probability of class i, and p_i is the predicted probability of class i. Categorical cross-entropy penalizes the model for making incorrect predictions, and encourages the model to assign high probabilities to the correct classes and low probabilities to the incorrect classes.
For the use case of building a model that predicts whether images contain a driver's license, passport, or credit card, categorical cross-entropy is the appropriate loss function to use. This is because the problem is a multi-class classification problem, where the target variable has three possible values: ['drivers_license',
'passport', 'credit_card']. The label map is a list that maps the class names to the class indices, such that
'drivers_license' corresponds to index 0, 'passport' corresponds to index 1, and 'credit_card' corresponds to index 2. The model should output a probability distribution over the three classes for each image, and the categorical cross-entropy loss function should compare the output with the true labels. Therefore, categorical cross-entropy is the best loss function for this use case.

NEW QUESTION # 58
You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b 'c'1, [d 'e T]]})
B. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
C. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c 'd'], ['e T]]})
D. data = json.dumps({"signature_name": "serving_default' "instances": [fab', 'be1, 'cd']]})

Answer: A

NEW QUESTION # 59
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?

A. Vary features independently to identify the threshold per feature that changes the classification.
B. Use the feature importance percentages in the model evaluation page.
C. Use the correlation with target values in the data summary page.
D. Use local feature importance from the predictions.

Answer: D

Explanation:
* Option A is correct because using local feature importance from the predictions is the best way to provide the reasons that contributed to the model's decision for a specific customer's loan request. Local feature importance is a measure of how much each feature affects the prediction for a given instance, relative to the average prediction for the dataset1. AutoML Tables provides local feature importance values for each prediction, which can be accessed using the Vertex AI SDK for Python or the Cloud Console2. By using local feature importance, you can explain why the model rejected the loan request based on the customer's data.
* Option B is incorrect because using the correlation with target values in the data summary page is not a good way to provide the reasons that contributed to the model's decision for a specific customer's loan request. The correlation with target values is a measure of how much each feature is linearly related to the target variable for the entire dataset, not for a single instance3. The data summary page in AutoML Tables shows the correlation with target values for each feature, as well as other statistics such as mean, standard deviation, and histogram4. However, these statistics are not useful for explaining the model's decision for a specific customer, as they do not account for the interactions between features or the non-linearity of the model.
* Option C is incorrect because using the feature importance percentages in the model evaluation page is not a good way to provide the reasons that contributed to the model's decision for a specific customer's loan request. The feature importance percentages are a measure of how much each feature affects the overall accuracy of the model for the entire dataset, not for a single instance5. The model evaluation page in AutoML Tables shows the feature importance percentages for each feature, as well as other metrics such as precision, recall, and confusion matrix. However, these metrics are not useful for explaining the model's decision for a specific customer, as they do not reflect the individual contribution of each feature for a given prediction.
* Option D is incorrect because varying features independently to identify the threshold per feature that changes the classification is not a feasible way to provide the reasons that contributed to the model's decision for a specific customer's loan request. This method involves changing the value of one feature at a time, while keeping the other features constant, and observing how the prediction changes.
However, this method is not practical, as it requires making multiple prediction requests, and may not capture the interactions between features or the non-linearity of the model.
References:
* Local feature importance
* Getting local feature importance values
* Correlation with target values
* Data summary page
* Feature importance percentages
* [Model evaluation page]
* [Varying features independently]

NEW QUESTION # 60
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