Professional Machine Learning Engineer 実際試験問題と解答 - 模擬練習 - 受験自信を増やす

Question No : 1
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time .
What should they use to track and report their experiments while minimizing manual effort?

A.Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines AP
B.Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAP
C.Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring AP
D.Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API

正解:

Question No : 2
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential
customers .
What factors should you consider before building the model?

A.Redaction, reproducibility, and explainability
B.Traceability, reproducibility, and explainability
C.Federated learning, reproducibility, and explainability
D.Differential privacy federated learning, and explainability

正解:

Question No : 3
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories .
What should you do?

A.Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
B.Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
C.Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
D.Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

正解:

Question No : 4
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.Create alerts to monitor for skew, and retrain the model.
B.Perform feature selection on the model, and retrain the model with fewer features
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 on a monthly basis with fewer features

正解:

Question No : 5
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 the class distribution to generate 10% positive examples
B.Use a convolutional neural network with max pooling and softmax activation
C.Downsample the data with upweighting to create a sample with 10% positive examples
D.Remove negative examples until the numbers of positive and negative examples are equal

正解:

Question No : 6
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66% .
How can you make your production model more accurate?

A.Normalize the data for the training, and test datasets as two separate steps.
B.Split the training and test data based on time rather than a random split to avoid leakage
C.Add more data to your test set to ensure that you have a fair distribution and sample for testing
D.Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

正解:

Question No : 7
During batch training of a neural network, you notice that there is an oscillation in the loss .
How should you adjust your model to ensure that it converges?

A.Increase the size of the training batch
B.Decrease the size of the training batch
C.Increase the learning rate hyperparameter
D.Decrease the learning rate hyperparameter

正解:

Question No : 8
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud .
What should you do?

A.Use Al Platform for distributed training
B.Create a cluster on Dataproc for training
C.Create a Managed Instance Group with autoscaling
D.Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

正解:

Question No : 9
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook .
What should you do?

A.Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
B.Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
C.Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
D.From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe

正解:

Question No : 10
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting .
Which strategy should you use when retraining the model?

A.Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
B.Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
C.Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
D.Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

正解:

Question No : 11
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model .
What should you do?

A.Modify the 'epochs' parameter
B.Modify the 'scale-tier' parameter
C.Modify the batch size' parameter
D.Modify the 'learning rate' parameter

正解:

Question No : 12
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production .
What is the most streamlined and reliable way to perform this validation?

A.Use the TFX ModelValidator tools to specify performance metrics for production readiness
B.Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
C.Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
D.Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

正解:

Question No : 13
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 number of parallel trials
B.Decrease the range of floating-point values
C.Set the early stopping parameter to TRUE
D.Change the search algorithm from Bayesian search to random search.
E.Decrease the maximum number of trials during subsequent training phases.

正解:

Question No : 14
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code .
What should you do?

A.Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
B.Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
C.Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
D.Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

正解:

Question No : 15
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard .
What should you do?

A.Create multiple models using AutoML Tables
B.Automate multiple training runs using Cloud Composer
C.Run multiple training jobs on Al Platform with similar job names
D.Create an experiment in Kubeflow Pipelines to organize multiple runs

正解:

Google Professional Machine Learning Engineer 問題練習