最新的Google Professional-Machine-Learning-Engineer免費考試真題

問題1

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

A. Develop an image segmentation ML model to locate the boundaries of the rust spots.

B. Develop a template matching algorithm using traditional computer vision libraries.

C. Develop an image classification ML model to predict the presence of the disease.

D. Create an object detection model that can localize the rust spots.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題2

You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

B. Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.

C. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

D. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.

正確答案: D

說明：（僅 Fast2test 成員可見）

問題3

You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Use Dataflow with the SavedModel to read the data from BigQuery

B. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

C. Deploy and version the model on Al Platform.

D. Export the model to BigQuery ML.

正確答案: B

說明：（僅 Fast2test 成員可見）

問題4

You need to deploy a scikit-learn classification model to production. The model must be able to serve requests
24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

A. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
1.

B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100

C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
100.

D. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1

正確答案: B

說明：（僅 Fast2test 成員可見）

問題5

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?

A. Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

B. Export the historical data to Cloud Storage in CSV format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

C. Configure and deploy a Vertex Al endpoint. Use the endpoint to get predictions from the historical data in BigQuery.

D. Import the TensorFlow model by using the create model statement in BigQuery ML. Apply the historical data to the TensorFlow model.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題6

You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

B. Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.

C. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

D. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.

正確答案: D

問題7

You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps
1. Randomly split the data into training and evaluation datasets in a 65/35 ratio
2. Conduct feature engineering
3 Obtain metrics for the evaluation dataset.
4 Compare models trained in different pipeline executions
How should you execute these steps ' ?

A. 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits.
2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table
' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.

B. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2. Enable auto logging of metrics in the training component.
3 Compare pipeline runs in Vertex Al Experiments

C. 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits.
2 Use a SQL view to apply feature engineering and train the model using the data in that view
3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.

D. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2 Enable autologging of metrics in the training component
3 Compare models using the artifacts lineage in Vertex ML Metadata

正確答案: D

說明：（僅 Fast2test 成員可見）

問題8

You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

A. Use a convolutional neural network with max pooling and softmax activation

B. Use the class distribution to generate 10% positive examples

C. Remove negative examples until the numbers of positive and negative examples are equal

D. Downsample the data with upweighting to create a sample with 10% positive examples

正確答案: D

說明：（僅 Fast2test 成員可見）

最新的Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer免費考試真題

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