Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer Free Exam Questions

A. Reduce the image shape

B. Change the learning rate

C. Change the optimizer

D. Reduce the batch size

A. Change the machine type to e2-highcpu-32 with 32 vCPUs and 32 GB of memory.

B. Set up a monitoring job and an alert for CPU usage. If you receive an alert, scale the vCPUs as needed.

C. Re-deploy the model with a TPU accelerator.

D. Increase the number of maximum replicas to 6 nodes, each with 1 e2-standard-2 machine.

A. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.

B. Schedule a weekly query in BigQuery to compute the success metric.

C. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.

D. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.

A. * Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

B. * Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

C. * Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

D. * Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

A. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.

B. Create a new view with BigQuery that does not include a column with city information

C. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

D. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

A. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.

B. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.

C. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.

D. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.

A. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2. Enable auto logging of metrics in the training component.
3 Compare pipeline runs in Vertex Al Experiments

B. 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits.
2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table
' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.

C. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
2 Enable autologging of metrics in the training component
3 Compare models using the artifacts lineage in Vertex ML Metadata

D. 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits.
2 Use a SQL view to apply feature engineering and train the model using the data in that view
3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.

A. Export the historical data to Cloud Storage in CSV format Configure a Vertex Al batch prediction job to generate predictions for the exported data.

B. Configure a Vertex Al batch prediction job to apply the model to the historical data in BigQuery

C. Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

D. Import the TensorFlow model by using the create model statement in BigQuery ML Apply the historical data to the TensorFlow model.

A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving

B. Significantly increase the max_enqueued_batches TensorFlow Serving parameter

C. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

D. Significantly increase the max_batch_size TensorFlow Serving parameter

A. 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.

B. 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

C. 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.

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

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

A. Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

B. Enable autoscaling of the online serving nodes in your featurestore

C. Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.

D. Increase the worker counts in the importFeaturevalues request of your batch ingestion job.

A. Containerize the PySpark transformation step, and add it to your pipeline.

B. Remove the data transformation step from your pipeline.

C. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.

D. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.