What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Duration of Exam: 120 minutes
- No negative marking for wrong answers
- Language of Exam: English, Japanese, Korean
- Type of Questions: Multiple choice (MCQs), multiple answers
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Topics of Professional Machine Learning Engineer - Google
Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:
- Data Preparation and Processing
- ML Problem Framing
- ML Pipeline Automation & Orchestration
- ML Solution Monitoring, Optimization, and Maintenance
- ML Model Development
- ML Solution Architecture
Professional Machine Learning Engineer - Google Certification Path
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Architecting low-code ML solutions | - AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Topic 2: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 3: Scaling prototypes into ML models | - Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) |
| Topic 4: Monitoring ML solutions | - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) - Model retraining strategies |
| Topic 5: Collaborating within and across teams to manage data and models | - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance |
| Topic 6: Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) |
Google Professional Machine Learning Engineer Sample Questions:
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
- Optimizer: SGD
- Image shape = 224-224
- Batch size = 64
- Epochs = 10
- Verbose =2
During training you encounter the following error: ResourceExhaustedError: Out Of Memory (OOM) when allocating tensor. What should you do?
- A. Reduce the batch size.
- B. Reduce the image shape.
- C. Change the learning rate.
- D. Change the optimizer.
Correct Answer: A 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
You work at an ecommerce startup. You need to create a customer churn prediction model. Your company's recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?
- A. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
- B. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.
- C. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
- D. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.
Correct Answer: D 🗳️
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. 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 AI training job.
- B. 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 AI training job.
- C. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the DataflowPythonJobOp component to preprocess the data and the CustomTrainingJobOp component to launch a Vertex AI 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 AI training job.
Correct Answer: D 🗳️
You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline:
ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint.
You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?
- A. Use Cloud Composer for the orchestration.
- B. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
- C. Use Kubeflow Pipelines on Google Kubernetes Engine.
- D. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
You run nightly training jobs that take approximately nine hours on eight GPUs. The jobs are not time-critical and can restart if interrupted, but a full restart from scratch wastes significant budget.
You need to reduce training cost substantially. What should you do?
- A. Reduce the number of GPUs to two to lower the hourly rate, and accept the longer runtime.
- B. Run the jobs on on-demand GPUs, and purchase committed use discounts for the full eight-GPU configuration.
- C. Move the training job to a Workbench instance that you shut down manually after each run.
- D. Configure the custom training job to use Spot VMs, enable job scheduling with restart on failure, and write periodic checkpoints to Cloud Storage so training resumes from the last checkpoint.
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
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