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

Section	Objectives
Monitoring ML solutions	- Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring)
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)
Automating and orchestrating ML pipelines	- Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems
Serving and scaling models	- Online prediction (Vertex AI Prediction) - Batch prediction - Model optimization (Quantization, Distillation) - Hardware accelerators (GPU/TPU) in serving
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
Scaling prototypes into ML models	- Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)

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Google Professional Machine Learning Engineer Sample Questions (Q127-Q132):

NEW QUESTION # 127
You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?

A. 1. Create a new endpoint.
2. Create a new model Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version Upload the model to Vertex Al Model Registry
3. Deploy the new model to the new endpoint and set the new model to 100% of the traffic
B. 1 Create a new endpoint.
2 Create a new model Set it as the default version Upload the model to Vertex Al Model Registry.
3. Deploy the new model to the new endpoint.
4 Update Cloud DNS to point to the new endpoint
C. 1 Create a new model Set the parentModel parameter to the model ID of the currently deployed model Upload the model to Vertex Al Model Registry.
2 Deploy the new model to the existing endpoint and set the new model to 100% of the traffic.
D. 1, Create a new model Set it as the default version Upload the model to Vertex Al Model Registry
2 Deploy the new model to the existing endpoint

Answer: C

NEW QUESTION # 128
You recently deployed a model lo a Vertex Al endpoint and set up online serving in Vertex Al Feature Store.
You have configured a daily batch ingestion job to update your featurestore During the batch ingestion jobs you discover that CPU utilization is high in your featurestores online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?

A. Increase the worker counts in the importFeaturevalues request of your batch ingestion job.
B. Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.
C. Enable autoscaling of the online serving nodes in your featurestore
D. Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.

Answer: C

Explanation:
Vertex AI Feature Store provides two options for online serving: Bigtable and optimized online serving. Both options support autoscaling, which means that the number of online serving nodes can automatically adjust to the traffic demand. By enabling autoscaling, you can improve the online serving performance and reduce the feature retrieval latency during the daily batch ingestion. Autoscaling also helps you optimize the cost and resource utilization of your featurestore. References:
* Online serving | Vertex AI | Google Cloud
* New Vertex AI Feature Store: BigQuery-Powered, GenAI-Ready | Google Cloud Blog

NEW QUESTION # 129
You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator; estimator = tf.estimator.DNNRegressor( feature_columns=[YOUR_LIST_OF_FEATURES], hidden_units-[1024, 512, 256], dropout=None) Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

A. Switch from CPU to GPU serving
B. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
C. Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters
D. Increase the dropout rate to 0.8 and retrain your model.

Answer: B

Explanation:
Applying quantization to your SavedModel by reducing the floating point precision can help reduce the serving latency by decreasing the amount of memory and computation required to make a prediction. TensorFlow provides tools such as the tf.quantization module that can be used to quantize models and reduce their precision, which can significantly reduce serving latency without a significant decrease in model performance.

NEW QUESTION # 130
You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

A. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
B. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
C. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
D. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline.
Use Matplotlib to visualize metrics.

Answer: A

NEW QUESTION # 131
You work for a multinational organization that has recently begun operations in Spain. Teams within your organization will need to work with various Spanish documents, such as business, legal, and financial documents. You want to use machine learning to help your organization get accurate translations quickly and with the least effort. Your organization does not require domain- specific terms or jargon. What should you do?

A. Create a Vertex AI Workbench notebook instance. In the notebook, extract sentences from the documents, and train a custom AutoML text model.
B. Create a Vertex AI Workbench notebook instance. In the notebook, convert the Spanish documents into plain text, and create a custom TensorFlow seq2seq translation model.
C. Use the Document Translation feature of the Cloud Translation API to translate the documents.
D. Use Google Translate to translate 1,000 phrases from Spanish to English. Using these translated pairs, train a custom AutoML Translation model.

Answer: C

Explanation:
This option provides a straightforward solution for translating various types of documents (business, legal, financial) quickly and with minimal effort. It leverages Google's Cloud Translation API, which is designed specifically for tasks like this and eliminates the need for manual training or customization.
https://cloud.google.com/translate/docs

NEW QUESTION # 132
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