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Google Professional Machine Learning Engineer certification exam is a valuable credential for machine learning engineers, data scientists, and software engineers who want to demonstrate their proficiency in designing, building, and deploying scalable and efficient machine learning models on the Google Cloud Platform. By passing the certification exam, candidates will be able to distinguish themselves from their peers and demonstrate their expertise in the field of machine learning.

To be eligible for the Google Professional Machine Learning Engineer certification exam, candidates must have a minimum of three years of experience in the field of machine learning. Candidates should also have experience in designing and implementing machine learning solutions using Google Cloud technologies such as Google Cloud ML Engine, BigQuery, and TensorFlow. In addition to these requirements, candidates should have a strong understanding of machine learning algorithms and data structures.

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Google Professional Machine Learning Engineer Certification Exam is an important credential for professionals who want to demonstrate their expertise in machine learning. Professional-Machine-Learning-Engineer Exam covers a range of topics and requires significant preparation, but it is a valuable asset for professionals who want to advance their career in machine learning. With this certification, candidates can showcase their skills and knowledge to potential employers and demonstrate their ability to design and develop machine learning models on the Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q23-Q28):

NEW QUESTION # 23
You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?

A. Use Dataflow with the SavedModel to read the data from BigQuery.
B. Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.
C. Deploy and version the model on AI Platform.
D. Export the model to BigQuery ML.

Answer: D

Explanation:
https://cloud.google.com/bigquery-ml/docs/making-predictions-with-imported-tensorflow-models#importing_models

NEW QUESTION # 24
You have trained an XGBoost model that you plan to deploy on Vertex AI for online prediction.
You are now uploading your model to Vertex AI Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency.
You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

A. 1. Specify sampled Shapley as the explanation method with a path count of 5.
2. Deploy the model to Vertex AI Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
B. 1. Specify sampled Shapley as the explanation method with a path count of 50.
2. Deploy the model to Vertex AI Endpoints.
3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
C. 1. Specify Integrated Gradients as the explanation method with a path count of 5.
2. Deploy the model to Vertex AI Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
D. 1. Specify Integrated Gradients as the explanation method with a path count of 50.
2. Deploy the model to Vertex AI Endpoints.
3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

Answer: A

Explanation:
https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

NEW QUESTION # 25
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
B. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
C. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Answer: A

Explanation:
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

NEW QUESTION # 26
You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A. Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution skew.
B. Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.
C. Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution drift.
D. Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.

Answer: C

Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
* Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is atechnique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
* Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
References:
* 1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
* 2: Shapley value - Wikipedia
* 3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
* 4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
* [5]: Monitor models for data drift | Vertex AI | Google Cloud

NEW QUESTION # 27
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

Answer: B

Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2 To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3 The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45

NEW QUESTION # 28
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