Online Access Free Professional-Machine-Learning-Engineer Practice Test

You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

• A.Import the TensorFlow model with BigQuery ML, and run the ml.predict function.
• B.Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
• C.Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
• D.Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.

You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table.
The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:
You launch your Vertex Al pipeline as the following:
You perform many model iterations by adjusting the code and parameters of the training step.
You observe high costs associated with the development, particularly the data export and preprocessing steps.
You need to reduce model development costs.
What should you do?

• A.
• B.
• C.
• D.

You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

• A.Store image files in Cloud Filestore, and access them by using serialized records.
• B.Store image files in Cloud Filestore and access them directly by using an NFS mount point.
• C.Store image files in Cloud Storage and access them directly.
• D.Store image files in Cloud Storage and access them by using serialized records.

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

You have created a Vertex Al pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket The second step uses the processed data to train a model You need to update the model's code to allow you to test different algorithms You want to reduce pipeline execution time and cost, while also minimizing pipeline changes What should you do?

• A.Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.
• B.Add a pipeline parameter and an additional pipeline step Depending on the parameter value the pipeline step conducts or skips data preprocessing and starts model training.
• C.Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.
• D.Enable caching for the pipeline job. and disable caching for the model training step.