Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Exam Dumps

You are training an LSTM-based model on AI Platform to summarize text using the following job submission script:

gcloud ai-platform jobs submit training $JOB_NAME \

--package-path $TRAINER_PACKAGE_PATH \

--module-name $MAIN_TRAINER_MODULE \

--job-dir $JOB_DIR \

--region $REGION \

--scale-tier basic \

-- \

--epochs 20 \

--batch_size=32 \

--learning_rate=0.001 \

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.

You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries: CREATE OR REPLACE TABLE `myproject.mydataset.training` AS (SELECT * FROM `myproject.mydataset.mytable` WHERE RAND() <= 0.8);

CREATE OR REPLACE TABLE `myproject.mydataset.validation` AS (SELECT * FROM `myproject.mydataset.mytable` WHERE RAND() <= 0.2);

After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

A. There is training-serving skew in your production environment.

B. There is not a sufficient amount of training data.

C. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.

D. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

A. 1. Create a Vertex AI managed dataset.

2.

Use a Vertex AI training pipeline to train your model.

3.

Generate batch predictions in Vertex AI.

B. 1. Use a Vertex AI Pipelines custom training job component to tram your model.

2. Generate predictions by using a Vertex AI Pipelines model batch predict component.

C. 1. Upload your dataset to BigQuery.

2.

Use a Vertex AI custom training job to train your model.

3.

Generate predictions by using Vertex Al SDK custom prediction routines.

D. 1. Use Vertex AI Experiments to train your model.

2.

Register your model in Vertex AI Model Registry.

3.

Generate batch predictions in Vertex AI.