1043 お客様のコメント
質問 1：
You recently deployed a model to a Vertex AI endpoint and set up online serving in Vertex AI Feature Store. You have configured a daily batch ingestion job to update your featurestore.
During the batch ingestion jobs, you discover that CPU utilization is high in your featurestore's online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?
A. Increase the worker_count in the ImportFeatureValues request of your batch ingestion job
B. Enable autoscaling of the online serving nodes in your featurestore
C. Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint
D. Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs
正解：B
質問 2：
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?
A. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
C. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID.
Query the lookup table to find the correct table ID for the data that you need.
D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases. You have unstructured textual data with custom labels. You need to extract and classify various medical phrases with these labels. What should you do?
A. Use the Healthcare Natural Language API to extract medical entities
B. Use a BERT-based model to fine-tune a medical entity extraction model
C. Use AutoML Entity Extraction to train a medical entity extraction model
D. Use TensorFlow to build a custom medical entity extraction model
正解：C
質問 4：
You started 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 the model overfitting by using a less complex algorithm.
C. Address data leakage by removing features highly correlated with the target value.
D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible.
What should you do?
A. Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
B. Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.
C. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.
D. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.
正解：C
質問 6：
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.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)
You recently deployed a model to a Vertex AI endpoint and set up online serving in Vertex AI Feature Store. You have configured a daily batch ingestion job to update your featurestore.
During the batch ingestion jobs, you discover that CPU utilization is high in your featurestore's online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?
A. Increase the worker_count in the ImportFeatureValues request of your batch ingestion job
B. Enable autoscaling of the online serving nodes in your featurestore
C. Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint
D. Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs
正解：B
質問 2：
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?
A. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
B. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
C. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID.
Query the lookup table to find the correct table ID for the data that you need.
D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)
質問 3：
You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases. You have unstructured textual data with custom labels. You need to extract and classify various medical phrases with these labels. What should you do?
A. Use the Healthcare Natural Language API to extract medical entities
B. Use a BERT-based model to fine-tune a medical entity extraction model
C. Use AutoML Entity Extraction to train a medical entity extraction model
D. Use TensorFlow to build a custom medical entity extraction model
正解：C
質問 4：
You started 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 the model overfitting by using a less complex algorithm.
C. Address data leakage by removing features highly correlated with the target value.
D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)
質問 5：
You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible.
What should you do?
A. Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
B. Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.
C. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.
D. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.
正解：C
質問 6：
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.
正解：D
解説: (Pass4Test メンバーにのみ表示されます)
高桥** -
Professional-Machine-Learning-Engineer合格いたしました。Pass4Testさんほんとうにすごい。このProfessional-Machine-Learning-Engineer問題集で受かりそうです。