Google Professional-Machine-Learning-Engineer Practice Test : Free Online Preparation

Question 1
- You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels What should you do?
  
  A : Use the Healthcare Natural Language API to extract medical entities.
  
  B : Use a BERT-based model to fine-tune a medical entity extraction model.
  
  C : Use AutoML Entity Extraction to train a medical entity extraction model.
  
  D : Use TensorFlow to build a custom medical entity extraction model.
  
  Answer: B
Question 2
- 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
Question 3
- Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers' account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?
  
  A : 1. Create a Pub/Sub topic for each user. 2. Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
  
  B : 1. Create a Pub/Sub topic for each user. 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
  
  C : 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.
  
  D : 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
  
  Answer: A
Question 4
- You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
  
  A : Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
  
  B : Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
  
  C :
  Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
  
  D :
  Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
  
  Answer: C
Question 5
- You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?
  
  A : Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.
  
  B : Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.
  
  C : Use the CREATE MODEL statement and select the categorical and non-categorical features.
  
  D : Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
  
  Answer: D

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