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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

>> Professional-Machine-Learning-Engineer최고기출문제 <<

Professional-Machine-Learning-Engineer최고기출문제 시험준비에 가장 좋은 인기덤프

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최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q343-Q348):

질문 # 343
You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

• A. Load the data into a pandas DataFrame Implement the preprocessing steps using panda's transformations. and train the model directly on the DataFrame.
• B. Perform preprocessing in BigQuery by using SQL Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.
• C. Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc Save the preprocessed data as CSV files in a Cloud Storage bucket.
• D. Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow Save the preprocessed data as CSV files in a Cloud Storage bucket.

정답：B

설명：
* Option A is not the best answer because it requires using Apache Spark and Dataproc, which may incur additional cost and complexity for running and managing the cluster. It also requires saving the preprocessed data as CSV files in a Cloud Storage bucket, which may increase the storage cost and the data transfer latency.
* Option B is not the best answer because it requires loading the data into a pandas DataFrame, which may not be scalable or efficient for large datasets. It also requires training the model directly on the DataFrame, which may not leverage the distributed computing capabilities of BigQuery.
* Option C is the best answer because it allows performing preprocessing in BigQuery by using SQL, which is a cost-effective and efficient way to manipulate large datasets. It also allows using the BigQueryClient in TensorFlow to read the data directly from BigQuery, which is a convenient and fast way to access the data for training the model1.
* Option D is not the best answer because it requires using Apache Beam and Dataflow, which may incur additional cost and complexity for running and managing the pipeline. It also requires saving the preprocessed data as CSV files in a Cloud Storage bucket, which may increase the storage cost and the data transfer latency.
References:
* 1: Read data from BigQuery | TensorFlow I/O

질문 # 344
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. One feature obtained as an element-wise product between 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. 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
• D. Three individual features binned latitude, binned longitude, and one-hot encoded car type

정답：A

설명：
A feature cross is a synthetic feature that is obtained by combining two or more existing features, usually by taking their product or concatenation. A feature cross can help to capture the nonlinear and interaction effects between the original features, and improve the predictive performance of the model. A feature cross can be applied to different types of features, such as numeric, categorical, or geospatial features 1 .
For the use case of building an ML model to predict car sales in different cities around the world, the best option is to use one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type. This option involves creating a feature cross that combines three individual features: binned latitude, binned longitude, and one-hot encoded car type. Binning is a technique that transforms a continuous numeric feature into a discrete categorical feature by dividing its range into equal intervals, or bins. One-hot encoding is a technique that transforms a categorical feature into a binary vector, where each element corresponds to a possible category, and has a value of 1 if the feature belongs to that category, and 0 otherwise. By applying binning and one-hot encoding to the latitude, longitude, and car type features, the feature cross can capture the city-specific relationships between car type and number of sales, as each combination of bins and car types can represent a different city and its preference for a certain car type.
For example, the feature cross can learn that a city with a latitude bin of [40, 50], a longitude bin of [-80, -70]
, and a car type of SUV has a higher number of sales than a city with a latitude bin of [-10, 0] , a longitude bin of [10, 20], and a car type of sedan. Therefore, using one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type is the best option for this use case.
References:
Feature Crosses | Machine Learning Crash Course

질문 # 345
You recently deployed a scikit-learn model to a Vertex Al endpoint You are now testing the model on live production traffic While monitoring the endpoint. you discover twice as many requests per hour than expected throughout the day You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency What should you do?

• A. Change the model's machine type to one that utilizes GPUs.
• B. Configure an appropriate minReplicaCount value based on expected baseline traffic.
• C. Set the target utilization percentage in the autcscalir.gMetricspecs configuration to a higher value
• D. Deploy two models to the same endpoint and distribute requests among them evenly.

정답：B

설명：
The best option for scaling a Vertex AI endpoint efficiently when the demand increases in the future, using a scikit-learn model that is deployed to a Vertex AI endpoint and tested on live production traffic, is to configure an appropriate minReplicaCount value based on expected baseline traffic. This option allows you to leverage the power and simplicity of Vertex AI to automatically scale your endpoint resources according to the traffic patterns. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained model to an online prediction endpoint, which can provide low-latency predictions for individual instances. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A minReplicaCount value is a parameter that specifies the minimum number of replicas that the endpoint must always have, regardless of the load. AminReplicaCount value can help you ensure that the endpoint has enough resources to handle the expected baseline traffic, and avoid high latency or errors. By configuring an appropriate minReplicaCount value based on expected baseline traffic, you can scale your endpoint efficiently when the demand increases in the future. You can set the minReplicaCount value when you deploy the model to the endpoint, or update it later. Vertex AI will automatically scale up or down the number of replicas within the range of the minReplicaCount and maxReplicaCount values, based on the target utilization percentage and the autoscaling metric1.
The other options are not as good as option B, for the following reasons:
* Option A: Deploying two models to the same endpoint and distributing requests among them evenly would not allow you to scale your endpoint efficiently when the demand increases in the future, and could increase the complexity and cost of the deployment process. A model is a resource that represents a machine learning model that you can use for prediction. A model can have one or more versions, which are different implementations of the same model. A model version can help you experiment and iterate on your model, and improve the model performance and accuracy. An endpoint is a resource that provides the service endpoint (URL) you use to request the prediction. An endpoint can have one or more deployed models, which are instances of model versions that are associated with physical resources. A deployed model can help you serve online predictions with low latency, and scale up or down based on the traffic. By deploying two models to the same endpoint and distributing requests among them evenly, you can create a load balancing mechanism that can distribute the traffic across the models, and reduce the load on each model. However, deploying two models to the same endpoint and distributing requests among them evenly would not allow you to scale your endpoint efficiently when the demand increases in the future, and could increase the complexity and cost of the deployment process. You would need to write code, create and configure the two models, deploy the models to the same endpoint, and distribute the requests among them evenly. Moreover, this option would not use the autoscaling feature of Vertex AI, which can automatically adjust the number of replicas based on the traffic patterns, and provide various benefits, such as optimal resource utilization, cost savings, and performance improvement2.
* Option C: Setting the target utilization percentage in the autoscalingMetricSpecs configuration to a higher value would not allow you to scale your endpoint efficiently when the demand increases in the future, and could cause errors or poor performance. A target utilization percentage is a parameter that specifies the desired utilization level of each replica. A target utilization percentage can affect the speed
* and accuracy of the autoscaling process. A higher target utilization percentage can help you reduce the number of replicas, but it can also cause high latency, low throughput, or resource exhaustion. By setting the target utilization percentage in the autoscalingMetricSpecs configuration to a higher value, you can increase the utilization level of each replica, and save some resources. However, setting the target utilization percentage in the autoscalingMetricSpecs configuration to a higher value would not allow you to scale your endpoint efficiently when the demand increases in the future, and could cause errors or poor performance. You would need to write code, create and configure the autoscalingMetricSpecs, and set the target utilization percentage to a higher value. Moreover, this option would not ensure that the endpoint has enough resources to handle the expected baseline traffic, which could cause high latency or errors1.
* Option D: Changing the model's machine type to one that utilizes GPUs would not allow you to scale your endpoint efficiently when the demand increases in the future, and could increase the complexity and cost of the deployment process. A machine type is a parameter that specifies the type of virtual machine that the prediction service uses for the deployed model. A machine type can affect the speed and accuracy of the prediction process. A machine type that utilizes GPUs can help you accelerate the computation and processing of the prediction, and handle more prediction requests at the same time. By changing the model's machine type to one that utilizes GPUs, you can improve the prediction performance and efficiency of your model. However, changing the model's machine type to one that utilizes GPUs would not allow you to scale your endpoint efficiently when the demand increases in the future, and could increase the complexity and cost of the deployment process. You would need to write code, create and configure the model, deploy the model to the endpoint, and change the machine type to one that utilizes GPUs. Moreover, this option would not use the autoscaling feature of Vertex AI, which can automatically adjust the number of replicas based on the traffic patterns, and provide various benefits, such as optimal resource utilization, cost savings, and performance improvement2.
References:
* Configure compute resources for prediction | Vertex AI | Google Cloud
* Deploy a model to an endpoint | Vertex AI | Google Cloud

질문 # 346
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy.
Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

• A. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
• B. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
• C. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
• D. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.

정답：A

설명：
The best option for performing hyperparameter tuning on Vertex AI to determine the appropriate embedding dimension and the optimal learning rate is to use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials. This option has the following advantages:
* It matches the appropriate scaling type for each hyperparameter, based on their range and distribution.
The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LINEAR_SCALE makes sense. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LOG_SCALE is more suitable.
* It maximizes the exploration of the hyperparameter space, by using a large number of parallel trials.
Since training time is not a concern, using more trials can help find the best combination of hyperparameters that maximizes model accuracy. The default Bayesian optimization tuning algorithm can efficiently sample the hyperparameter space and converge to the optimal values.
The other options are less optimal for the following reasons:
* Option B: Using UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials, reduces the exploration of the hyperparameter space, by using a small number of parallel trials. Since training time is not a concern, using fewer trials can miss some potentially good combinations of hyperparameters that maximize model accuracy. The default Bayesian optimization tuning algorithm can benefit from more trials to sample the hyperparameter space and converge to the optimal values.
* Option C: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials, mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LOG_SCALE is not suitable.
The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LINEAR_SCALE makes less sense.
* Option D: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials, combines the drawbacks of option B and option C. It mismatches the appropriate scaling type for each hyperparameter, based ontheir range and distribution, and reduces the exploration of the hyperparameter space, by using a small number of parallel trials.
References:
* [Vertex AI: Hyperparameter tuning overview]
* [Vertex AI: Configuring the hyperparameter tuning job]

질문 # 347
You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

• A. Use BigQuery ML to build a statistical AR1MA_PLUS model.
• B. Use Vertex Al Forecast to build a NN-based model.
• C. Use TensorFlow on Vertex Al Training to build a custom model.
• D. Use Prophet on Vertex Al Training to build a custom model.

정답：A

설명：
According to the web search results, BigQuery ML1 is a service that allows you to create and execute machine learning models in BigQuery using SQL queries. BigQuery ML supports various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, deep neural networks, and time series forecasting1. ARIMA_PLUS2 is a statistical model for time series forecasting that is built in to BigQuery ML. ARIMA_PLUS stands for AutoRegressive Integrated Moving Average with eXogenous regressors. ARIMA_PLUS models the relationship between a target variable and its past values, as well as other external factors that might influence the target variable. ARIMA_PLUS can handle multiple time series, seasonality, holidays, and missing values2. Therefore, option C is the best way to use a solution that can be implemented quickly with minimal effort for the given use case, as it allows you to use SQL queries to build and run a forecasting model in BigQuery without moving the data or writing custom code. The other options are not relevant or optimal for this scenario. Reference:
BigQuery ML
ARIMA_PLUS
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

질문 # 348
......

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