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Google Professional Machine Learning Engineer Exam is a highly sought-after certification in the field of machine learning. It is intended for professionals who have extensive experience in designing and implementing machine learning models and workflows using Google Cloud Platform technologies. Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preprocessing, feature engineering, model selection, hyperparameter tuning, model evaluation, and deployment. Passing Professional-Machine-Learning-Engineer Exam demonstrates that the candidate has the skills and knowledge required to design, develop, and deploy production-grade machine learning models on Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q32-Q37):

NEW QUESTION # 32
You developed a custom model by using Vertex Al to forecast the sales of your company s products based on historical transactional data You anticipate changes in the feature distributions and the correlations between the features in the near future You also expect to receive a large volume of prediction requests You plan to use Vertex Al Model Monitoring for drift detection and you want to minimize the cost. What should you do?

A. Use the features for monitoring Set a monitoring- frequency value that is higher than the default.
B. Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
C. Use the features and the feature attributions for monitoring Set a prediction-sampling-rate value that is closer to 0 than 1.
D. Use the features for monitoring Set a prediction-sampling-rare value that is closer to 1 than 0.

Answer: C

Explanation:
The best option for using Vertex AI Model Monitoring for drift detection and minimizing the cost is to use the features and the feature attributions for monitoring, and set a prediction-sampling-rate value that is closer to 0 than 1. This option allows you to leverage the power and flexibility of Google Cloud to detect feature drift in the input predict requests for custom models, and reduce the storage and computation costs of the model monitoring job. Vertex AI Model Monitoring is a service that can track and compare the results of multiple machine learning runs. Vertex AI Model Monitoring can monitor the model's prediction input data for feature skew and drift. Feature drift occurs when the feature data distribution in production changes over time. If the original training data is not available, you can enable drift detection to monitor your models for feature drift. Vertex AI Model Monitoring uses TensorFlow Data Validation (TFDV) to calculate the distributions and distance scores for each feature, and compares them with a baseline distribution. The baseline distribution is the statistical distribution of the feature's values in the training data. If the training data is not available, the baseline distribution is calculated from the first 1000 prediction requests that the model receives. If the distance score for a feature exceeds an alerting threshold that you set, Vertex AI Model Monitoring sends you an email alert. However, if you use a custom model, you can also enable feature attribution monitoring, which can provide more insights into the feature drift. Feature attribution monitoring analyzes the feature attributions, which are the contributions of each feature to the prediction output. Feature attribution monitoring can help you identify the features that have the most impact on the model performance, and the features that have the most significant drift over time. Feature attribution monitoring can also help you understand the relationship between the features and the prediction output, and the correlation between the features1. The prediction-sampling-rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a lower prediction-sampling-rate can reduce the storage and computation costs of the model monitoring job, but also the quality and validity of the data. Using a lower prediction-sampling-rate can introduce sampling bias and noise into the data, and make the model monitoring job miss some important features or patterns of the data. However, using a higher prediction-sampling-rate can increase the storage and computation costs of the model monitoring job, and also the amount of data that needs to be processed and analyzed. Therefore, there is a trade-off between the prediction-sampling-rate and the cost and accuracy of the model monitoring job, and the optimal prediction-sampling-rate depends on the business objective and the data characteristics2. By using the features and the feature attributions for monitoring, and setting a prediction-sampling-rate value that is closer to 0 than 1, you can use Vertex AI Model Monitoring for drift detection and minimize the cost.
The other options are not as good as option D, for the following reasons:
Option A: Using the features for monitoring and setting a monitoring-frequency value that is higher than the default would not enable feature attribution monitoring, and could increase the cost of the model monitoring job. The monitoring-frequency is a parameter that determines how often the model monitoring job analyzes the logged prediction requests and calculates the distributions and distance scores for each feature. Using a higher monitoring-frequency can increase the frequency and timeliness of the model monitoring job, but also the computation costs of the model monitoring job. Moreover, using the features for monitoring would not enable feature attribution monitoring, which can provide more insights into the feature drift and the model performance1.
Option B: Using the features for monitoring and setting a prediction-sampling-rate value that is closer to 1 than 0 would not enable feature attribution monitoring, and could increase the cost of the model monitoring job. The prediction-sampling-rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a higher prediction-sampling-rate can increase the quality and validity of the data, but also the storage and computation costs of the model monitoring job. Moreover, using the features for monitoring would not enable feature attribution monitoring, which can provide more insights into the feature drift and the model performance12.
Option C: Using the features and the feature attributions for monitoring and setting a monitoring-frequency value that is lower than the default would enable feature attribution monitoring, but could reduce the frequency and timeliness of the model monitoring job. The monitoring-frequency is a parameter that determines how often the model monitoring job analyzes the logged prediction requests and calculates the distributions and distance scores for each feature. Using a lower monitoring-frequency can reduce the computation costs of the model monitoring job, but also the frequency and timeliness of the model monitoring job. This can make the model monitoring job less responsive and effective in detecting and alerting the feature drift1.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 4: Evaluation Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.3 Monitoring ML models in production Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.3: Monitoring ML Models Using Model Monitoring Understanding the score threshold slider

NEW QUESTION # 33
You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?

A. Store parameters in Vertex ML Metadata store the models' source code in GitHub and store the models' binaries in Cloud Storage.
B. Store parameters in Cloud SQL store the models' source code in GitHub, and store the models' binaries in Cloud Storage.
C. Store parameters in Vertex ML Metadata and store the models source code and binaries in GitHub.
D. Store parameters in Cloud SQL and store the models' source code and binaries in GitHub

Answer: A

Explanation:
To organize the artifacts for dozens of pipelines, you should store the parameters in Vertex ML Metadata, store the models' source code in GitHub, and store the models' binaries in Cloud Storage. This option has the following advantages:
Vertex ML Metadata is a service that helps you track and manage the metadata of your ML workflows, such as datasets, models, metrics, and parameters1. It can also help you with data lineage, model versioning, and model performance monitoring2.
GitHub is a popular platform for hosting and collaborating on code repositories. It can help you manage the source code of your models, as well as the configuration files, scripts, and notebooks that are part of your ML pipelines3.
Cloud Storage is a scalable and durable object storage service that can store any type of data, including model binaries4. It can also integrate with other services, such as Vertex AI, Cloud Functions, and Cloud Run, to enable easy deployment and serving of your models5.
Reference:
1: Introduction to Vertex ML Metadata | Vertex AI | Google Cloud
2: Manage metadata for ML workflows | Vertex AI | Google Cloud
3: GitHub - Where the world builds software
4: Cloud Storage | Google Cloud
5: Deploying models | Vertex AI | Google Cloud

NEW QUESTION # 34
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 custom PyTorch regression model, and optimize it using Vertex Al Training
B. Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.
C. Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training
D. Develop a regression model using BigQuery ML.

Answer: D

Explanation:
BigQuery ML is a powerful tool that allows you to build and deploy machine learning models directly within BigQuery, Google's fully-managed, serverless data warehouse. It allows you to create regression models using SQL, which is a familiar and easy-to-use language for many data scientists. It also allows you to scale smoothly and require minimal development work since you don't have to worry about cluster management and it's fully-managed by Google.
BigQuery ML also allows you to run your training on the same data where it's stored, this will minimize data movement, and thus minimize cost and time.
Reference:
BigQuery ML
BigQuery ML for regression
BigQuery ML for scalability

NEW QUESTION # 35
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

A. * Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
B. * Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction
C. * Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
D. * Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

Answer: C

Explanation:
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing

NEW QUESTION # 36
While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A. Add parallel interleave to the pipeline
B. Add caching to the pipeline
C. Increase the network bandwidth
D. Increase the CPU load

Answer: D

NEW QUESTION # 37
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