Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer

You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?

• A. Run a Dataflow streaming job to ingest the data into BigQuery.
• B. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
• C. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
• D. Configure Pub/Sub to stream the data into BigQuery.

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and
1,000 images with credit cards. You now have to train a model with the following label map:
[`drivers_license', `passport', `credit_card'].
Which loss function should you use?

• A. Sparse categorical cross-entropy
• B. Categorical cross-entropy
• C. Categorical hinge
• D. Binary cross-entropy

You have deployed a custom classification model to an Agent Platform endpoint to predict the likelihood of customer churn in real-time. You need to collect the data to calculate the model's exact precision and recall by comparing production predictions with ground truth data in BigQuery. You want to minimize development effort. What should you do?

• A. Update the model deployment parameters to enable request-response logging to BigQuery. Set the sampling rate to 1.0.
• B. Create a model evaluation job by providing the model resource and the BigQuery ground truth table to calculate precision and recall.
• C. Schedule a daily Agent Platform batch prediction job that uses the same model resource as the deployed endpoint and stores predictions in BigQuery.
• D. Modify the prediction container to asynchronously write the prediction result to BigQuery after every request.

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 Vertex AI Experiments to train your model.
  2. Register your model in Vertex AI Model Registry.
  3. Generate batch predictions in Vertex AI.
• 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 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.

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

• A. Use TensorFlow I/O's BigQuery Reader to directly read the data.
• B. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.
• C. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.
• D. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.from_tensor_slices() to read it.