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To earn the Google Professional Machine Learning Engineer certification, candidates must pass a rigorous exam that tests their ability to design, implement, and optimize machine learning models using Google Cloud Platform. Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preparation, model training, model evaluation, and deployment strategies. In addition, the exam also tests candidates' ability to optimize models for performance and scalability, as well as their knowledge of ethical and responsible AI practices.

The Google Professional Machine Learning Engineer certification exam consists of multiple-choice questions that assess the candidate's knowledge of machine learning concepts, data preprocessing, model selection, hyperparameter tuning, model evaluation, and deployment. Professional-Machine-Learning-Engineer exam is conducted online and is proctored by a third-party vendor. Candidates are required to pass the exam within two hours and thirty minutes and must score at least 80% to pass.



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The Google Professional-Machine-Learning-Engineer exam is designed to test a variety of skills and knowledge areas related to machine learning, including data analysis, model selection and evaluation, and deployment and monitoring of machine learning models. It is also designed to test candidates' ability to apply machine learning techniques to real-world problems and to demonstrate their ability to work effectively with data science teams.

Google Professional Machine Learning Engineer Sample Questions (Q215-Q220):

NEW QUESTION # 215
You are training and deploying updated versions of a regression model with tabular data by using Vertex Al Pipelines. Vertex Al Training Vertex Al Experiments and Vertex Al Endpoints. The model is deployed in a Vertex Al endpoint and your users call the model by using the Vertex Al endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model What should you do?

* A. Export the container logs of the endpoint to BigQuery Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.
* B. In Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.
* C. In Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.
* D. Use Vertex Al Model Monitoring Enable prediction drift monitoring on the endpoint. and specify a notification email.
Answer: D

NEW QUESTION # 216
You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?

* A. Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data process it and write it back to BigQuery.
* B. Write SQL queries to transform the data in-place in BigQuery.
* C. Add the transformations as a preprocessing layer in the TensorFlow models.
* D. Write the transformations into Spark that uses the spark-bigquery-connector and use Dataproc to preprocess the data.
Answer: B

NEW QUESTION # 217
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

* A. Cloud Composer, BigQuery ML , and Al Platform Prediction
* B. Vertex AI Pipelines and Al Platform Prediction
* C. Cloud Composer, Al Platform Training with custom containers, and App Engine
* D. Vertex AI Pipelines and App Engine
Answer: B

Explanation:
Vertex AI Pipelines and AI Platform Prediction are the platform components that best suit the requirements of the data science team. Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning workflows using pipelines. Pipelines are portable and scalable ML workflows that are based on containers. You can use Vertex AI Pipelines to schedule model retraining, use custom containers, and integrate with other Google Cloud services. AI Platform Prediction is a service that allows you to host your trained models and serve online predictions. You can use AI Platform Prediction to deploy models trained on Vertex AI or elsewhere, and benefit from features such as autoscaling, monitoring, logging, and explainability. References:
* Vertex AI Pipelines
* AI Platform Prediction

NEW QUESTION # 218
You developed a Transformer model in TensorFlow to translate text Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the clusters configuration. What should you do?

* A. Create a Vertex Al custom training job with GPU accelerators for the second worker pool Use tf
.distribute.MultiWorkerMirroredStrategy for distribution.
* B. Create a Vertex Al custom training job with a single worker pool of A2 GPU machine type instances Use tf .distribute.MirroredStraregy for distribution.
* C. Create a Vertex Al custom distributed training job with Reduction Server Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.
* D. Create a training job that uses Cloud TPU VMs Use tf.distribute.TPUStrategy for distribution.
Answer: D

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "configure and optimize model training jobs". Cloud TPU VMs2 are a new way to access Cloud TPUs directly on the TPU host machines, offering a simpler and more flexible user experience. Cloud TPU VMs are optimized for ML model training and can reduce training time and cost. You can use Cloud TPU VMs to train Transformer models in TensorFlow by using the tf.distribute.TPUStrategy3, which handles the distribution of computations across the TPU cores. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Cloud TPU VMs
* Distributed training with TPUStrategy
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 219
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

* A. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
* B. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
* C. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
* D. Schedule a weekly query in BigQuery to compute the success metric.
Answer: D

Explanation:
The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages:
* It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline.
* It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost.
* It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuery's built-in scheduling feature. You can also set up alerts or
* notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly.
The other options are less optimal for the following reasons:
* Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of
100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring.
* Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost. Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations.
* Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost.
References:
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NEW QUESTION # 220
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