Google Google Certifications PROFESSIONAL-MACHINE-LEARNING-ENGINEER Questions & Answers

• Question 151:

  You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:

  1.

  Check for availability of the movie tickets at the selected cinema.

  2.

  Assign the ticket price and accept payment.

  3.

  Reserve the tickets at the selected cinema.

  4.

  Send successful purchases to your database.

  Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

  A. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.

  B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.

  C. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.

  D. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.

  Correct Answer: D

  https://www.tensorflow.org/lite/guide

• Question 152:

  You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model's performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?

  A. Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service.

  B. Identify temporal patterns in your model's performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year.

  C. Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.

  D. Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service.

  Correct Answer: D

  https://cloud.google.com/blog/topics/developers-practitioners/monitor-models-training-serving-skew-vertex-aiew-vertex-aiandved=2ahUKEwiRg_aoj9n8AhWb7TgGHcGCDREQFnoECAwQAQandusg=AOvVaw197NneIJM0ra7fLq2zsOin

• Question 153:

  You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

  A. Embed the augmentation functions dynamically in the tf.Data pipeline.

  B. Embed the augmentation functions dynamically as part of Keras generators.

  C. Use Dataflow to create all possible augmentations, and store them as TFRecords.

  D. Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.

  Correct Answer: A

  https://www.tensorflow.org/tutorials/load_data/images#using_tfdata_for_finer_control

• Question 154:

  You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

  A. Use the func_to_container_op function to create custom components from the Python code.

  B. Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.

  C. Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.

  D. Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.

  Correct Answer: A

  https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html?highlight=func_to_container_op%20#kfp.components.func_to_container_op