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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Deployment and operations	- Model deployment 1. Batch and online prediction systems 2. Deploy models using Vertex AI endpoints - Monitoring and maintenance 1. Retraining and lifecycle management 2. Monitor model drift and performance
Designing ML solutions	- Framing ML problems 1. Translate business problems into ML tasks 2. Define success metrics and evaluation criteria - ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches
Data preparation and processing	- Feature engineering 1. Feature selection and representation techniques 2. Transform and preprocess datasets - Data ingestion and pipelines 1. Use BigQuery and data processing services 2. Build data pipelines for training and serving
ML pipeline automation and orchestration	- Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines
ML model development	- Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics

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Google Professional Machine Learning Engineer Sample Questions (Q363-Q368):

NEW QUESTION # 363
You work for a retail company. You have created a Vertex Al forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

A. Create a batch prediction job by using the actual sales data Compare the predictions to the actuals in the report.
B. Create a batch prediction job by using the actual sates data and configure the job settings to generate feature attributions. Compare the results in the report.
C. Train another model by using the same training dataset as the original and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model Compare the two sets of predictions in the report.
D. Generate counterfactual examples by using the actual sales data Create a batch prediction job using the actual sales data and the counterfactual examples Compare the results in the report.

Answer: B

NEW QUESTION # 364
You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

A. Use the TensorFlow Extended SDK to implement the pipeline Use the Examplegen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex Al training job.
B. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the dataflowpythonjobopcomponent to preprocess the data and the customTraining JobOp component to launch a Vertex Al training job.
C. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex Al training job.
D. Use the Kubeflow Pipelines SDK to implement the pipeline Use the BigQueryJobop component to run the preprocessing script and the customTrainingJobop component to launch a Vertex Al training job.

Answer: A

Explanation:
* Explanation: TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more. TFX components can also leverage other Google Cloud services, such as BigQuery, Dataflow, and Vertex AI.
* Why not A: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the BigQueryJobOp component to run the preprocessing script is not optimal. This would require writing and maintaining a separate SQL script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
* Why not B: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the DataflowPythonJobOp component to preprocess the data is not optimal. This would require writing and maintaining a separate Python script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
* Why not D: Using the TensorFlow Extended SDK to implement the pipeline is a valid option, but implementing the preprocessing steps as part of the input_fn of the model is not optimal. This would make the preprocessing logic tightly coupled with the model code, which could reduce modularity and flexibility. It would also make it harder to reuse the same preprocessing logic for both training and serving.

NEW QUESTION # 365
You work at an organization that manages a popular payment app. You built a fraudulent transaction detection model by using scikit-learn and deployed it to a Vertex AI endpoint. The endpoint is currently using 1 e2-standard-2 machinewith 2 vCPUs and 8 GB of memory.
You discover that traffic on the gateway fluctuates to four times more than the endpoint's capacity. You need to address this issue by using the most cost-effective approach. What should you do?

A. Increase the number of maximum replicas to 6 nodes, each with 1 e2-standard-2machine.
B. Change the machine type to e2-highcpu-32with 32 vCPUs and 32 GB of memory.
C. Re-deploy the model with a TPU accelerator.
D. Set up a monitoring job and an alert for CPU usage. If you receive an alert, scale the vCPUs as needed.

Answer: A

Explanation:
Increasing the number of replicas allows the endpoint to handle fluctuating traffic by distributing requests across multiple instances of the same machine type. This horizontal scaling is more cost-effective than upgrading to a much larger machine or using specialized accelerators when the workload is CPU-bound and fluctuating.

NEW QUESTION # 366
You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do? (Choose Correct Answer and Give Reference and Explanation)

A. Configure a Compute Engine VM with all the dependencies that launches the training Train your model with Vertex Al using a custom tier that contains the required GPUs.
B. Package your code with Setuptools. and use a pre-built container Train your model with Vertex Al using a custom tier that contains the required GPUs.
C. Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model
D. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

Answer: D

Explanation:
Google Kubernetes Engine (GKE) is a powerful and easy-to-use platform for deploying and managing containerized applications. It allows you to create a cluster of virtual machines that are pre-configured with the necessary dependencies and resources to run your machine learning workloads. By creating a GKE cluster with a node pool that has 4 V100 GPUs, you can take advantage of the powerful processing capabilities of these GPUs to train your model quickly and efficiently.
You can then use the Kubernetes Framework such as TFJob operator to submit the job of training your model, which will automatically distribute the workload across the available GPUs.
Reference:
Google Kubernetes Engine
TFJob operator
Vertex Al

NEW QUESTION # 367
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Develop a regression model using BigQuery ML.
B. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
D. Train a regression model using AutoML Tables.

Answer: A

Explanation:
The key is to understand the amount of data that needs to be used for training - the sensor collects tens of millions of records every day and the model needs to use all the data up to the current date.
There is a limitation for AutoML is 100M rows -> https://cloud.google.com/vertex-ai/docs/tabular- data/classification-regression/prepare-data

NEW QUESTION # 368
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