Professional-Machine-Learning-Engineer無料問題集「Google Professional Machine Learning Engineer」

質問 1

You work for a telecommunications company You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery, and the predictive features that are available for model training include
- Customer_id -Age
- Salary (measured in local currency) -Sex
-Average bill value (measured in local currency)
- Number of phone calls in the last month (integer) -Average duration of phone calls (measured in minutes) You need to investigate and mitigate potential bias against disadvantaged groups while preserving model accuracy What should you do?

（A）Train a BigQuery ML boosted trees classification model with all features Use the ml. exflain_predict method to calculate the attribution values for each feature for each customer in a test set If for any individual customer the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.

（B）Determine whether there is a meaningful correlation between the sensitive features and the other features Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features

（C）Define a fairness metric that is represented by accuracy across the sensitive features Train a BigQuery ML boosted trees classification model with all features Use the trained model to make predictions on a test set Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.

（D）Train a BigQuery ML boosted trees classification model with all features Use the ml. global explain method to calculate the global attribution values for each feature of the model If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

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）Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.

（B）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.

（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 解答を投票する

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質問 3

You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

（A）Decrease the maximum number of trials during subsequent training phases.

（B）Set the early stopping parameter to TRUE

（C）Decrease the number of parallel trials

（D）Change the search algorithm from Bayesian search to random search.

（E）Decrease the range of floating-point values

正解：A、B 解答を投票する

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質問 4

You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

（A）Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).

（B）Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().

（C）Create a tf.data.Dataset.prefetch transformation

（D）Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

（A）Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

（B）Create alerts to monitor for skew, and retrain the model.

（C）Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

（D）Perform feature selection on the model, and retrain the model with fewer features

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min- max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?

（A）Write the transformations into Spark that uses the spark-bigquery-connector and use Dataproc to preprocess the data.

（B）Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data process it and write it back to BigQuery.

（C）Write SQL queries to transform the data in-place in BigQuery.

（D）Add the transformations as a preprocessing layer in the TensorFlow models.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?

（A）Create a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

（B）Train an AutoML image classification model.

（C）Create a Vertex Al pipeline that runs different model training jobs in parallel.

（D）Create a Vertex Al hyperparameter tuning job.

正解：D 解答を投票する

質問 8

You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

（A）Load each model's individual files into Cloud Run Use Cloud Run to orchestrate the workflow Track the versions of each individual model in BigQuery.

（B）Create a custom container endpoint for the workflow that loads each models individual files Track the versions of each individual model in BigQuery.

（C）Expose each individual model as an endpoint in Vertex Al Endpoints. Create a custom container endpoint to orchestrate the workflow.

（D）Expose each individual model as an endpoint in Vertex Al Endpoints. Use Cloud Run to orchestrate the workflow.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability.
You want to productionize the pipeline as quickly as possible What should you do?

（A）Use Cloud Composer to build the training pipelines for custom deep learning-based models.

（B）Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and deep neural networks.

（C）Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.

（D）Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?

（A）Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.

（B）Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.

（C）Train an object detection model in Vertex Al custom training by using the annotated image data.

（D）Train an object detection model in AutoML by using the annotated image data.

正解：D 解答を投票する

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質問 11

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

（A）1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold

（B）1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

（C）1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

（D）1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

正解：C 解答を投票する

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質問 12

You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

（A）1 Write a SQL query to create a separate lookup table to scale the numerical features.
2 Perform the one-hot text encoding in BigQuery.
3. Feed the resulting BigQuery view into Vertex Al Training.

（B）1 Use TFX components with Dataflow to encode the text features and scale the numerical features.
2 Export results to Cloud Storage as TFRecords.
3 Feed the data into Vertex Al Training.

（C）1 Use BigQuery to scale the numerical features.
2. Feed the features into Vertex Al Training.
3 Allow TensorFlow to perform the one-hot text encoding.

（D）1 Write a SQL query to create a separate lookup table to scale the numerical features.
2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.
3. Feed the resulting BigQuery view into Vertex Al Training.

正解：B 解答を投票する

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質問 13

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 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language

（B）1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision

（C）1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API

（D）1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Professional-Machine-Learning-Engineer 無料問題集「Google Professional Machine Learning Engineer」

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