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

（A）Number of messages flagged by the model per minute

（B）Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review

（C）Number of messages flagged by the model per minute confirmed as being inappropriate by humans.

（D）Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

（A）Use Principal Component Analysis to eliminate the least informative features.

（B）After building your model, use Shapley values to determine which features are the most informative.

（C）Use L1 regularization to reduce the coefficients of uninformative features to 0.

（D）Use an iterative dropout technique to identify which features do not degrade the model when removed.

（A）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

（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 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

（A）Use Vertex Al Feature Store Modify the pipeline to use the feature store; and ensure that all training data is stored in it Search the feature store for the data used for the training.

（B）Use the lineage feature of Vertex Al Metadata to find the model artifact Determine the version of the model and identify the step that creates the data copy, and search in the metadata for its location.

（C）Find the job ID in Vertex Al Training corresponding to the training for the model Search in the logs of that job for the data used for the training.

（D）Use the logging features in the Vertex Al endpoint to determine the timestamp of the models deployment Find the pipeline run at that timestamp Identify the step that creates the data copy; and search in the logs for its location.

（A）Traceability, reproducibility, and explainability

（B）Differential privacy federated learning, and explainability

（C）Federated learning, reproducibility, and explainability

（D）Redaction, reproducibility, and explainability

（A）Create a batch prediction job by using the actual sales data Compare the predictions to the actuals in the report.

（B）Train another model by using the same training dataset as the original and exclude some columns.Using the actual sales data create one batch prediction job by using the new model and another one with the original model Compare the two sets of predictions in the report.

（C）Create a batch prediction job by using the actual sates data and configure the job settings to generate feature attributions. Compare the results in the report.

（D）Generate counterfactual examples by using the actual sales data Create a batch prediction job using the actual sales data and the counterfactual examples Compare the results in the report.

（A）Build a classification model

（B）Build a regression model using the features as predictors

（C）Build a collaborative-based filtering model

（D）Build a knowledge-based filtering model

（A）1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
2 Reference tf .data.TFRecordDataset in the training script.
3. Train the model by using Vertex Al Training with a V100 GPU.

（B）1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
2 Reference tfds.fclder_da-asst.imageFclder in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.

（C）1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.
2 Write a Python script that creates sharded TFRecord files in a directory inside the instance
3. Reference tf. da-a.TFRecrrdDataset in the training script.
4. Train the model by using the Workbench instance.

（D）1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.
2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
3 Reference tf ds. f older_dataset. imageFolder in the training script.
4. Train the model by using the Workbench instance.

（A）Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

（B）Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.

（C）Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.

（D）Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.

（E）Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket

（A）AI Platform Training built-in algorithms

（B）Cloud Natural Language API

（C）AI Hub pre-made Jupyter Notebooks

（D）AutoML Natural Language

（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.

（A）Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.

（B）Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.

（C）Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.

（D）Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.

（A）Apache Flink

（B）Dataflow

（C）Dataprep

（D）Cloud Data Fusion