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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Machine Learning Engineer |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Exam Price: | $200 USD |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer |
| Available Languages: | English, Japanese |
| Exam Format: | Multiple choice, Multiple select |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 50-60 |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online (proctored) or Test center (Kryterion) |
| Pre Condition: | Recommended 3+ years of industry experience with ML models and 1+ year of experience using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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Google Professional Machine Learning Engineer Certification Exam is a professional certification that tests the knowledge and skills of individuals in the field of machine learning. Professional-Machine-Learning-Engineer exam is designed to evaluate the proficiency of candidates in various aspects of machine learning, including data processing, modeling, and deployment. Google Professional Machine Learning Engineer certification is offered by Google Cloud, a subsidiary of Google that provides cloud computing services to businesses and individuals.
Google Professional Machine Learning Engineer exam is an advanced-level certification offered by Google Cloud. Google Professional Machine Learning Engineer certification is aimed at individuals who have extensive experience in machine learning and are seeking to become experts in this field. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's proficiency in designing, building, and deploying machine learning models using Google Cloud technologies.
NEW QUESTION # 75
You have recently trained a scikit-learn model that you plan to deploy on Vertex Al. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code What should you do?
Answer: D
Explanation:
The best option for deploying a scikit-learn model on Vertex AI with minimal additional code is to wrap the model in a custom prediction routine (CPR) and build a container image from the CPR local model. Upload your scikit-learn model container to Vertex AI Model Registry. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job. This option allows you to leverage the power and simplicity of Google Cloud to deploy and serve a scikit-learn model that supports both online and batch prediction. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction endpoint, which can provide low-latency predictions for individual instances. Vertex AI can also create a batch prediction job, which can provide high- throughput predictions for a large batch of instances. A custom prediction routine (CPR) is a Python script that defines the logic for preprocessing the input data, running the prediction, and postprocessing the output data. A CPR can help you customize the prediction behavior of your model, and handle complex or non- standard data formats. A CPR can also help you minimize the additional code, as you only need to write a few functions to implement the prediction logic. A container image is a package that contains the model, the CPR, and the dependencies. A container image can help you standardize and simplify the deployment process, as you only need to upload the container image to Vertex AI Model Registry, and deploy it to Vertex AI Endpoints. By wrapping the model in a CPR and building a container image from the CPR local model, uploading the scikit-learn model container to Vertex A I Model Registry, deploying the model to Vertex AI Endpoints, and creating a Vertex AI batch prediction job, you can deploy a scikit-learn model on Vertex AI with minimal additional code 1 .
The other options are not as good as option B, for the following reasons:
* Option A: Uploading your model to the Vertex AI Model Registry by using a prebuilt scikit-learn prediction container, deploying your model to Vertex AI Endpoints, and creating a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data would not allow you to preprocess the input data for model inference, and could cause errors or poor performance.
A prebuilt scikit-learn prediction container is a container image that is provided by Google Cloud, and contains the scikit-learn framework and the dependencies. A prebuilt scikit-learn prediction container can help you deploy a scikit-learn model without writing any code, but it also limits your customization options. A prebuilt scikit-learn prediction container can only handle standard data formats, such as JSON or CSV, and cannot perform any preprocessing or postprocessing on the input or output data. If your input data requires any transformation or normalization before running the prediction, you cannot use a prebuilt scikit-learn prediction container. The instanceConfig.instanceType setting is a parameter that determines the machine type and the accelerator type for the batch prediction job. The instanceConfig.instanceType setting can help you optimize the performance and the cost of the batch prediction job, but it cannot help you transform your input data 2 .
* Option C: Creating a custom container for your scikit-learn model, defining a custom serving function for your model, uploading your model and custom container to Vertex AI Model Registry, and deploying your model to Vertex AI Endpoints, and creating a Vertex AI batch prediction job would require more skills and steps than using a CPR and a container image. A custom container is a container image that contains the model, the dependencies, and a web server. A custom container can help you customize the prediction behavior of your model, and handle complex or non-standard data formats. A custom serving function is a Python function that defines the logic for running the prediction on the model. A custom serving function can help you implement the prediction logic of your model, and handle complex or non-standard data formats. However, creating a custom container and defining a custom serving function would require more skills and steps than using a CPR and a container image.
You would need to write code, build and test the container image, configure the web server, and implement the prediction logic. Moreover, creating a custom container and defining a custom serving function would not allow you to preprocess the input data for model inference, as the custom serving function only runs the prediction on the model 3 .
* Option D: Creating a custom container for your scikit-learn model, uploading your model and custom container to Vertex AI Model Registry, deploying your model to Vertex AI Endpoints, and creating a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data would not allow you to preprocess the input data for model inference, and could cause errors or poor performance. A custom container is a container image that contains the model, the dependencies, and a web server. A custom container can help you customize the prediction behavior of your model, and handle complex or non-standard data formats. However, creating a custom container would require more skills and steps than using a CPR and a container image. You would need to write code, build and test the container image, and configure the web server. The instanceConfig.
instanceType setting is a parameter that determines the machine type and the accelerator type for the batch prediction job. The instanceC onfig.instanceType setting can help you optimize the performance and the cost of the batch prediction job, but it cannot help you transform your input data 2 3 .
References:
Preparing for Goo gle Cloud Certification: Machine Learning Engineer , Course 3: Production ML Systems, Week 2: Serving ML Predictions Google Cloud Professional Machine Learning Engineer Exam Guide , Section 3: Scaling ML models in production, 3.1 Deploying ML models to production Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6: Production ML Systems, Section 6.2: Serving ML Predictions Custom prediction routines Using pre-built containers for predi ction Using custom containers for prediction
NEW QUESTION # 76
You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website.
You plan to deploy the model to a new Vertex Al endpoint that will integrate with your website application.
You expect higher traffic to the website during
nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?
Answer: C
Explanation:
Deploying a model to an endpoint in Vertex AI associates physical resources with the model so it can serve online predictions with low latency1. By configuring the model deployment settings to use an n1-standard-4 machine type and setting the minReplicaCount value to 1 and the maxReplicaCount value to 8, you can ensure that the model scales according to the traffic, thereby minimizing latency and cost1. The n1-standard-4 machine type provides a balance between computing power and cost, and the dynamic scaling allows the model to handle higher traffic during nights and weekends without incurring unnecessary costs during off-peak times
NEW QUESTION # 77
You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?
Answer: B
Explanation:
When you deploy a custom container to Vertex AI Model Registry, you need to follow some requirements for the container configuration. One of these requirements is to use the HTTP port 8080 for serving predictions. If you use a different port, the model server might not be able to communicate with Vertex AI and cause the error "Error model server never became ready". To fix this error, you need to change the HTTP port in your model's configuration to the default value of 8080 and redeploy the container. Reference:
Custom container requirements documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 78
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
Answer: D
Explanation:
Categorical cross-entropy is a loss function that is suitable for multi-class classification problems, where the target variable has more than two possible values. Categorical cross-entropy measures the difference between the true probability distribution of the target classes and the predicted probability distribution of the model. It is defined as:
L - sum(y_i * log(p_i))
where y_i is the true probability of class i, and p_i is the predicted probability of class i. Categorical cross-entropy penalizes the model for making incorrect predictions, and encourages the model to assign high probabilities to the correct classes and low probabilities to the incorrect classes.
For the use case of building a model that predicts whether images contain a driver's license, passport, or credit card, categorical cross-entropy is the appropriate loss function to use. This is because the problem is a multi-class classification problem, where the target variable has three possible values: ['drivers_license', 'passport', 'credit_card']. The label map is a list that maps the class names to the class indices, such that 'drivers_license' corresponds to index 0, 'passport' corresponds to index 1, and 'credit_card' corresponds to index 2. The model should output a probability distribution over the three classes for each image, and the categorical cross-entropy loss function should compare the output with the true labels. Therefore, categorical cross-entropy is the best loss function for this use case.
NEW QUESTION # 79
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?
Answer: C
Explanation:
Option A is incorrect because using batch prediction mode instead of online mode does not solve the "Out of Memory" error, but rather changes the latency and throughput of the prediction service. Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions1.
Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the "Out of Memory" error. The batch size is the number of instances that are processed together in one request. A smaller batch size means less data to load into memory at once2.
Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it. Base64 encoding is a way of representing binary data as ASCII characters, which increases the size of the data by about 33%3. Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV4.
Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the "Out of Memory" error, but rather increases the number of requests that can be sent to the prediction service per day. Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network5. Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.
Reference:
Choosing between online and batch prediction
Online prediction input data
Base64 encoding
Preparing data for prediction
Quotas and limits
NEW QUESTION # 80
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