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Google Professional Machine Learning Engineer exam is designed to test the knowledge and skills of professionals who are working in the field of machine learning. Professional-Machine-Learning-Engineer exam is considered to be one of the most challenging and comprehensive exams in the field of machine learning. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's ability to design, build, and deploy machine learning models using Google Cloud technologies.
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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.
The Google Professional-Machine-Learning-Engineer exam covers a wide range of topics including data preprocessing, model selection, feature engineering, hyperparameter tuning, and model deployment. Professional-Machine-Learning-Engineer Exam also tests the candidate's knowledge of machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, neural networks, and deep learning. To pass the exam, candidates must have a deep understanding of these topics and be able to demonstrate their practical application in real-world scenarios.
NEW QUESTION # 75
You are training an LSTM-based model on AI Platform to summarize text using the following job submission script:
gcloud ai-platform jobs submit training $JOB_NAME \
--package-path $TRAINER_PACKAGE_PATH \
--module-name $MAIN_TRAINER_MODULE \
--job-dir $JOB_DIR \
--region $REGION \
--scale-tier basic \
-- \
--epochs 20 \
--batch_size=32 \
--learning_rate=0.001 \
You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?
Answer: A
Explanation:
Changing the scale tier does not impact performance-only speeds up training time. Epochs, Batch size, and learning rate all are hyperparameters that might impact model accuracy.
NEW QUESTION # 76
You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with
8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?
Answer: C
Explanation:
Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with 8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage. Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources.
You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model. You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. References:
* [Vertex AI Endpoint documentation]
* [Autoscaling documentation]
* [Monitoring documentation]
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]
NEW QUESTION # 77
You recently deployed a model to a Vertex AI endpoint. Your data drifts frequently, so you have enabled request-response logging and created a Vertex AI Model Monitoring job. You have observed that your model is receiving higher traffic than expected. You need to reduce the model monitoring cost while continuing to quickly detect drift. What should you do?
Answer: B
NEW QUESTION # 78
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?
Answer: A
Explanation:
TFX provides a set of standard components for building end-to-end ML pipelines, including data validation and model analysis. Vertex AI Pipelines is a fully managed service for building and orchestrating machine learning pipelines on Google Cloud.
NEW QUESTION # 79
You need to build classification workflows over several structured datasets currently stored in BigQuery.
Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
Answer: B
Explanation:
AutoML Tables is a service that allows you to automatically build and deploy state-of-the-art machine learning models on structured data without writing code. You can use AutoML Tables to perform the following steps for the classification task:
* Exploratory data analysis: AutoML Tables provides a graphical user interface (GUI) and a command-line interface (CLI) to explore your data, visualize statistics, and identify potential issues.
* Feature selection: AutoML Tables automatically selects the most relevant features for your model based on the data schema and the target column. You can also manually exclude or include features, or create new features from existing ones using feature engineering.
* Model building: AutoML Tables automatically builds and evaluates multiple machine learning models using different algorithms and architectures. You can also specify the optimization objective, the budget, and the evaluation metric for your model.
* Training and hyperparameter tuning: AutoML Tables automatically trains and tunes your model using the best practices and techniques from Google's research and engineering teams. You can monitor the training progress and the performance of your model on the GUI or the CLI.
* Serving: AutoML Tables automatically deploys your model to a fully managed, scalable, and secure environment. You can use the GUI or the CLI to request predictions from your model, either online (synchronously) or offline (asynchronously).
References:
* [AutoML Tables documentation]
* [AutoML Tables overview]
* [AutoML Tables how-to guides]
NEW QUESTION # 80
......
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