Professional-Machine-Learning-Engineer Exam: Absolutely Free Online Practice

Question 1
- You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?
  
  A : Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.
  
  B : Write SQL queries to transform the data in-place in BigQuery.
  
  C : Add the transformations as a preprocessing layer in the TensorFlow models.
  
  D : Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.
  
  Answer: B
Question 2
- You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?
  
  A :
  1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
  2. Your application queries a Vertex AI endpoint where you deployed your model.
  3. Responses are received by the caller application as soon as the model produces the prediction.
  
  B :
  1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
  2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
  3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
  
  C :
  1. Export your data to Cloud Storage using Dataflow.
  2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
  3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL
  
  D :
  1. Export the data to Cloud Storage using the BigQuery command-line tool
  2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
  3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
  
  Answer: C
Question 3
- Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
  A : the Natural Language API to classify support requests.
  
  B : AutoML Natural Language to build the support requests classifier.
  
  C : an established text classification model on AI Platform to perform transfer learning
  
  D : an established text classification model on AI Platform as-is to classify support requests
  
  Answer: D
Question 4
- You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?
  
  A : Add a logging configuration to your application to emit logs to Cloud Logging.
  
  B : Change the HTTP port in your model's configuration to the default value of 8080
  
  C : Change the health Route value in your models configuration to /heal thcheck
  
  D :
  Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.
  
  Answer: D
Question 5
- You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user’s recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?
  
  A :
  Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.
  
  B :
  Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user's selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production
  
  C :
  Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.
  
  D :
  Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model
  
  Answer: C

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