Google Professional-Machine-Learning-Engineer 問題集

質問 1：
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?
A. Z-normalize all the numeric features.
B. Oversample the fraudulent transaction 10 times.
C. Use one-hot encoding on all categorical features.
D. Write your data in TFRecords.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You have recently trained a scikit-learn model that you plan to deploy on Vertex Al. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code What should you do?
A. 1 Upload your model to the Vertex Al Model Registry by using a prebuilt scikit-learn prediction container
2 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig.inscanceType setting to transform your input data
B. 1 Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model
2 Upload your sci-kit learn model container to Vertex Al Model Registry
3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job
C. 1 Create a custom container for your sci-kit learn model.
2 Upload your model and custom container to Vertex Al Model Registry
3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig. instanceType setting to transform your input data
D. 1. Create a custom container for your sci-kit learn model,
2 Define a custom serving function for your model
3 Upload your model and custom container to Vertex Al Model Registry
4 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost.
What should you do?
A. Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.
B. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.
C. Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.
D. Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You are developing an ML model in a Vertex Al Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?
A. 1 Create a Vertex Al pipeline Use the Dataset and Model artifact types from the Kubeflow Pipelines.
DSL as the inputs and outputs of the components in your pipeline.
2. In your training component use the Vertex Al SDK to create an experiment run Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.
B. 1. Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket and upload the models to Vertex Al Model Registry.
2 After a successful experiment create a Vertex Al pipeline.
C. 1 Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution.
2 After a successful experiment create a Vertex Al pipeline.
D. 1 Create a Vertex Al pipeline with parameters you want to track as arguments to your Pipeline Job Use the Metrics. Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.
2. Associate the pipeline with your experiment when you submit the job.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
A. Cloud Composer, BigQuery ML , and Al Platform Prediction
B. Vertex AI Pipelines and Al Platform Prediction
C. Vertex AI Pipelines and App Engine
D. Cloud Composer, Al Platform Training with custom containers, and App Engine
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?
A. Load the model directly into the Dataflow job as a dependency, and use it for prediction.
B. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
C. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
D. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.
正解：A
解説: (Topexam メンバーにのみ表示されます)