Pass the Google Machine Learning Engineer Professional-Machine-Learning-Engineer Questions and answers with CertsForce

According to the official exam guide 1 , one of the skills assessed in the exam is to “configure and optimize model monitoring jobs”. The Vertex AI Model Monitoring documentation states that “to reduce the cost of model monitoring, you can configure the sample rate of the requests that are logged and analyzed by model monitoring”. Therefore, decreasing the sample_rate parameter in the Randomsampleconfig of the monitoring job would reduce the model monitoring cost while continuing to quickly detect drift. The other options are not relevant or optimal for this scenario.  References :

Preparing for Google Cloud Certifi cation: Machine Learning Engineer Professional Certificate

Professional ML Engineer Exam Guide

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

[Vertex AI Model Monitoring]

The best option for handling missing values in a categorical feature is to replace them with a placeholder category indicating a missing value. This is a type of imputation, which is a method of estimating the missing values based on the observed data. Imputing the missing values with a placeholder category preserves the information that the data is missing, and avoids introducing bias or distortion in the feature distribution. It also allows the machine learning model to learn from the missingness pattern, and potentially use it as a predictor for the target variable. The other options are not suitable for handling missing values in a categorical feature, because:

Removing the rows with missing values and upsampling the dataset by 5% would reduce the size of the dataset and potentially lose important information. It would also introduce sampling bias and overfitting, as the upsampling process would create duplicate or synthetic observations that do not reflect the true population.

Replacing the missing values with the feature’s mean would not make sense for a categorical feature, as the mean is a numerical measure that does not capture the mode or frequency of the categories. It would also create a new category that does not exist in the original data, and might confuse the machine learning model.

Moving the rows with missing values to the validation dataset would compromise the validity and reliability of the model evaluation, as the validation dataset would not be representative of the test or production data. It would also reduce the amount of data available for training the model, and might introduce leakage or inconsistency between the training and validation datasets.  References :

Imputation of missing values

Effective Strategies to Handle Missing Values in Data Analysis

How to Handle Missing Values of Categorical Variables?

Google Cloud launches machine learning engineer certification

Google Professional Machine L earning Engineer Certification

Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

The best option for choosing a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by the model address an imminent machine failure is to choose the model with the highest recall where precision is greater than 0.5. This option has the following advantages:

It maximizes the recall, which is the proportion of actual failures that are correctly predicted by the model. Recall is also known as sensitivity or true positive rate (TPR), and it is calculated as:

mathrmRecall=fracmathrmTPmathrmTP+mathrmFN

where TP is the number of true positives (actual failures that are predicted as failures) and FN is the number of false negatives (actual failures that are predicted as non-failures). By maximizing the recall, the model can reduce the number of false negatives, which are the most costly and undesirable outcomes for the predictive maintenance use case, as they represent missed failures that can lead to machine breakdown and downtime.

It constrains the precision, which is the proportion of predicted failures that are actual failures. Precision is also known as positive predictive value (PPV), and it is calculated as:

mathrmPrecision=fracmathrmTPmathrmTP+mathrmFP

where FP is the number of false positives (actual non-failures that are predicted as failures). By constraining the precision to be greater than 0.5, the model can ensure that more than 50% of the maintenance jobs triggered by the model address an imminent machine failure, which can avoid unnecessary or wasteful maintenance costs.

The other options are less optimal for the following reasons:

Option A: Choosing the model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5 may not prioritize detection, as the AUC ROC does not directly measure the recall. The AUC ROC is a summary metric that evaluates the overall performance of a binary classifier across all possible thresholds. The ROC curve plots the TPR (recall) against the false positive rate (FPR), which is the proportion of actual non-failures that are incorrectly predicted by the model. The AUC ROC is the area under the ROC curve, and it ranges from 0 to 1, where 1 represents a perfect classifier. However, choosing the model with the highest AUC ROC may not maximize the recall, as the AUC ROC is influenced by both the TPR and the FPR, and it does not account for the precision or the specificity (the proportion of actual non-failures that are correctly predicted by the model).

Option B: Choosing the model with the lowest root mean squared error (RMSE) and recall greater than 0.5 may not prioritize detection, as the RMSE is not a suitable metric for binary classification. The RMSE is a regression metric that measures the average magnitude of the error between the predicted and the actual values. The RMSE is calculated as:

mathrmRMSE=sqrtfrac1nsumi=1n​(yi​−hatyi​)2

where yi​ is the actual value, hatyi​ is the predicted value, and n is the number of observations. However, choosing the model with the lowest RMSE may not optimize the detection of failures, as the RMSE is sensitive to outliers and does not account for the class imbalance or the cost of misclassification.

Option D: Choosing the model with the highest precision where recall is greater than 0.5 may not prioritize detection, as the precision may not be the most important metric for the predictive maintenance use case. The precision measures the accuracy of the positive predictions, but it does not reflect the sensitivity or the coverage of the model. By choosing the model with the highest precision, the model may sacrifice the recall, which is the proportion of actual failures that are correctly predicted by the model. This may increase the number of false negatives, which are the most costly and undesirable outcomes for the predictive maintenance use case, as they represent missed failures that can lead to machine breakdown and downtime.

According to the official exam guide 1 , one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”.  Dataflow 2  is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataflow supports both batch and streaming data processing pipelines. However, if your use case requires real-time inference, you need to ensure that the data preprocessing logic is applied consistently between training and serving. One way to achieve this is to refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline, and use the same code in the endpoint. This way, you can avoid data skew and drift issues that might arise from using different preprocessing methods for training and serving. Therefore, option B is the best way to ensure the data preprocessing logic is applied consistently between training and serving. The other options are not relevant or optimal for this scenario.  References :

Professional ML Engineer Exam Guide

Dataflow

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Monitoring model performance is an essential part of production readiness, as it allows the team to detect and address any issues that may arise after deployment, such as data drift, model degradation, or errors.

Other Options :

A. Ensuring that training is reproducible is important for model development, but not necessarily for production readiness. Reproducibility helps the team to track and compare different experiments, but it does not guarantee that the model will perform well in production.

B. Ensuring that all hyperparameters are tuned is also important for model development, but not sufficient for production readiness. Hyperparameter tuning helps the team to find the optimal configuration for the model, but it does not account for the dynamic and changing nature of the production environment.

D. Ensuring that feature expectations are captured in the schema is a part of testing features and data, which the team has already done. The schema defines the expected format, type, and range of the features, and helps the team to validate and preprocess the data.

The best option for deploying a custom tabular ML model to production for online predictions, and monitoring the feature distribution over time with minimal effort, using a model that was provided by the bank’s vendor, the training data is not available due to its sensitivity, and the model is packaged as a Vertex AI Model serving container which accepts a string as input for each prediction instance, is to upload the model to Vertex AI Model Registry and deploy the model to a Vertex AI endpoint, create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema. This option allows you to leverage the power and simplicity of Vertex AI to serve and monitor your model with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained model to an online prediction endpoint, which can provide low-latency predictions for individual instances. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A Vertex AI Model Registry is a resource that can store and manage your models on Vertex AI. A Vertex AI Model Registry can help you organize and track your models, and access various model information, such as model name, model description, and model labels. A Vertex AI Model serving container is a resource that can run your custom model code on Vertex AI. A Vertex AI Model serving container can help you package your model code and dependencies into a container image, and deploy the container image to an online prediction endpoint. A Vertex AI Model serving container can accept various input formats, such as JSON, CSV, or TFRecord. A string input format is a type of input format that accepts a string as input for each prediction instance. A string input format can help you encode your feature values into a single string, and separate them by commas. By uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, you can serve your model for online predictions with minimal code and configuration. You can use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, and provide the model name, model description, and model labels. You can also use the Vertex AI API or the gcloud command-line tool to deploy the model to a Vertex AI endpoint, and provide the endpoint name, endpoint description, endpoint labels, and endpoint resources. A Vertex AI Model Monitoring job is a resource that can monitor the performance and quality of your deployed models on Vertex AI. A Vertex AI Model Monitoring job can help you detect and diagnose issues with your models, such as data drift, prediction drift, training/serving skew, or model staleness. Feature drift is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model over time. Feature drift can indicate that the online data is changing over time, and the model performance is degrading. By creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, you can monitor the feature distribution over time with minimal effort. You can use the Vertex AI API or the gcloud command-line tool to create a Vertex AI Model Monitoring job, and provide the monitoring objective, the monitoring frequency, the alerting threshold, and the notification channel. You can also provide an instance schema, which is a JSON file that describes the features and their types in the prediction input data.  An instance schema can he lp Vertex AI Model Monitoring parse and analyze the string input format, and calculate the feature distributions and distance scores 1 .

The other options are not as good as option A, for the following reasons:

Option B: Uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema would not help you monitor the changes in the online data over time, and could cause errors or poor performance. Feature skew is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model at a given point in time. Feature skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema, you can monitor the feature distribution at a given point in time with minimal effort. However, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema would not help you monitor the changes in the online data over time, and could cause errors or poor performance. You would need to use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, create a Vertex AI Model Monitoring job, and provide an instance schema.  Moreover, this option would not monitor the feature drift, which is a more direct and relevant metric for measuring the changes in the online data over time, and the model pe rformance and quality 1 .

Option C: Refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema. A key-value pair input format is a type of input format that accepts a key-value pair as input for each prediction instance. A key-value pair input format can help you specify the feature names and values in a JSON object, and separate them by colons. By refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, you can serve and monitor your model with minimal code and configuration. You can write code to refactor the serving container to accept key-value pairs as input format, and use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job. However, refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema. You would need to write code, refactor the serving container, upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job.  Moreover, this option would not use the instance schema, which is a JSON file that can help Vertex AI Model Monitoring parse and analyze the string input format, and calcula te the feature distributions and distance scores 1 .

Option D: Refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, and would not help you monitor the changes in the online data over time, and could cause errors or poor performance. Feature skew is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model at a given point in time. Feature skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, you can monitor the feature distribution at a given point in time with minimal effort. However, refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, and would not help you monitor the changes in the online data over time, and could cause errors or poor performance. You would need to write code, refactor the serving container, upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job.  Moreover, this option woul d not monitor the feature drift, which is a more direct and relevant metric for measuring the changes in the online data over time, and the model performance and quality 1 .

Vertex AI Pipelines (based on Kubeflow) provides Google Cloud Pipeline Components (GCPC) . These are pre-built, optimized components that perform common tasks.

Pre-built Components: ModelUploadOp and ModelDeployOp are native components specifically designed for these two steps. Chaining them together in your pipeline definition is the " simplest " approach because you don ' t have to write any custom Docker containers or manage Python client logic manually.

Why other options are incorrect:

Options A and B: These require writing custom code and maintaining container images, which increases complexity and maintenance overhead compared to using the built-in operators.

Option D: ModelEvaluationOp is used for assessing model quality (calculating metrics like AUC/Recall), not for the action of uploading or deploying a model.

 The trace in the question shows that the training time is taking longer than expected. This is likely due to the input function not being optimized. To decrease training time in a cost-efficient way, the best option is to rewrite the input function using parallel reads, parallel processing, and prefetch. This will allow the model to process the data more efficiently and decrease training time.  References :

[Cloud TPU Performance Guide]

[Data input pipeline performance guide]

To create a Vertex AI Workbench environment for your team and limit access to other employees in your project, you should follow these steps:

Create a new service account and grant it the Vertex AI User role.  This role grants full access to all resources in Vertex AI, including creating and managing notebook instances 1 .

Grant the Service Account User role to each team member on the service account.  This role allow s the team members to impersonate the service account and use its permissions 2 .

Grant the Notebook Viewer role to each team member.  This role allows the team members to view and connect to the notebook instance, but not to modify or delete it 3 .

Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account. This way, the notebook instance will run as the service account and only the team members who have the Service Account User and Notebook Viewer roles will be able to access it.

Vertex AI is a unified platform for building and deploying ML models on Google Cloud. It supports both custom and AutoML models, and provides various tools and services for ML development, such as Vertex Pipelines, Vertex Vizier, Vertex Explainable AI, and Vertex Feature Store. Vertex AI can be used to create models for predicting ticket priority and resolution time, as these are domain-specific tasks that require custom training data and evaluation metrics. Cloud Natural Language API is a pre-trained service that provides natural language understanding capabilities, such as sentiment analysis, entity analysis, syntax analysis, and content classification. Cloud Natural Language API can be used to perform sentiment analysis on the support tickets, as this is a general task that does not require domain-specific knowledge or jargon. The other options are not suitable for the given architecture. AutoML Natural Language and AutoML Vision are services that allow users to create custom natural language and vision models using their own data and labels. They are not needed for sentiment analysis, as Cloud Natural Language API already provides this functionality. Cloud Vision API is a pre-trained service that provides image analysis capabilities, such as object detection, face detection, text detection, and image labeling. It is not relevant for the support tickets, as they are not expected to have any images.  References:

Vertex AI documentation

Cloud Natural Language API documentation