Professional Machine Learning Engineer - Google Cloud Certified Exam Questions

A- A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

B- B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station. 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.

C- C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

D- D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

A- A. Use the class distribution to generate 10% positive examples.

B- B. Use a convolutional neural network with max pooling and softmax activation.

C- C. Downsample the data with upweighting to create a sample with 10% positive examples.

D- D. Remove negative examples until the numbers of positive and negative examples are equal.

A- A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.

B- B. Convert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.

C- C. Ingest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning.

D- D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.

A- 1 = Dataflow, 2 = Vertex AI, 3 = BigQuery

B- 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

C- 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

D- 1 = BigQuery, 2 = Vertex AI, 3 = Cloud Storage

A- Ensure that you have GPU quota in the selected region.

B- Ensure that the required GPU is available in the selected region.

C- Ensure that you have preemptible GPU quota in the selected region.

D- Ensure that the selected GPU has enough GPU memory for the workload.

A- A. Set up a cell to run a hyperparameter tuning job using Vertex AI Vizier with val_rmse specified as the metric in the study configuration.

B- B. Using a dedicated Python library such as Hyperopt or Optuna, configure a cell to run a local hyperparameter tuning job with Bayesian optimization.

C- C. Refactor the notebook into a parametrized and dockerized Python script, and push it to Container Registry. Use the UI to set up a hyperparameter tuning job in Vertex AI. Use the created image and include Grid Search as an algorithm.

D- D. Refactor the notebook into a parametrized and dockerized Python script, and push it to Container Registry. Use the command line to set up a hyperparameter tuning job in Vertex AI. Use the created image and include Random Search as an algorithm where maximum trial count is equal to parallel trial count.

A- Set up restrictive IAM permissions on the Vertex AI notebooks so that only a single user or group can access a given instance.

B- Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

C- Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

D- Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Vertex AI resource usage. In BigQuery, create a SQL view that maps users to the resources they are using.

A- C. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.

B- D. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.

C- A. AVM on Compute Engine and 1 TPU with all dependencies installed manually.

D- B. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.

A- A. 1. Use the Vertex AI SDK to create an experiment for the pipeline runs, and save metadata throughout the pipeline. 2. Configure a scheduled recurring execution for the notebook. 3. Access data and model metadata in Vertex ML Metadata.

B- B. 1. Use the Vertex AI SDK to create an experiment, launch a custom training job in Vertex training service with the same instance type configuration as the notebook, and save metadata throughout the pipeline. 2. Configure a scheduled recurring execution for the notebook. 3. Access data and model metadata in Vertex ML Metadata.

C- C. 1. Create a Cloud Storage bucket to store metadata. 2. Write a function that saves data and model metadata by using TensorFlow ML Metadata in one time-stamped subfolder per pipeline run. 3. Configure a scheduled recurring execution for the notebook. 4. Access data and model metadata in Cloud Storage.

D- D. 1. Refactor the pipeline code into a TensorFlow Extended (TFX) pipeline. 2. Load the TFX pipeline in Vertex AI Pipelines, and configure the pipeline to use the same instance type configuration as the notebook. 3. Use Cloud Scheduler to configure a recurring execution for the pipeline. 4. Access data and model metadata in Vertex AI Pipelines.

A- A. Address the model overfitting by using a less complex algorithm.

B- B. Address data leakage by applying nested cross-validation during model training.

C- C. Address data leakage by removing features highly correlated with the target value.

D- D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.