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Google Professional Machine Learning Engineer Certification Exam has become increasingly popular among individuals who are looking to enhance their career in machine learning engineering. Google Professional Machine Learning Engineer certification is recognized globally and is highly valued by employers. It helps professionals to develop their skills and knowledge in the field of machine learning engineering and provides them with an opportunity to showcase their expertise to potential employers. With the increasing demand for machine learning engineers, obtaining this certification can help individuals to stand out in the competitive job market.

Google Professional Machine Learning Engineer Sample Questions (Q283-Q288):

NEW QUESTION # 283
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

• A. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
• B. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
• C. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
• D. Classification month-to-month using supervised learning of the 200 categories based on claim contents.

Answer: B

Explanation:
Explanation

NEW QUESTION # 284
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

• A. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
• B. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
• C. An optimization objective that minimizes Log loss
• D. An optimization objective that maximizes the Precision at a Recall value of 0.50

Answer: B

Explanation:
In this scenario, the goal is to create a custom fraud detection model using AutoML Tables. Fraud detection is a type of binary classification problem, where the model needs to predict whether a transaction is fraudulent or not. The optimization objective is a metric that defines how the model is trained and evaluated. AutoML Tables allows you to choose from different optimization objectives for binary classification problems, such as Log loss, Precision at a Recall value, AUC PR, and AUC ROC.
To choose the best optimization objective for fraud detection, we need to consider the characteristics of the problem and the data. Fraud detection is a problem where the positive class (fraudulent transactions) is very rare compared to the negative class (legitimate transactions). This means that the data is highly imbalanced, and the model needs to be sensitive to the minority class. Moreover, fraud detection is a problem where the cost of false negatives (missing a fraudulent transaction) is much higher than the cost of false positives (flagging a legitimate transaction as fraudulent). This means that the model needs to have high recall (the ability to detect all fraudulent transactions) while maintaining high precision (the ability to avoid false alarms).
Given these considerations, the best optimization objective for fraud detection is the one that maximizes the area under the precision-recall curve (AUC PR) value. The AUC PR value is a metric that measures the trade- off between precision and recall for different probability thresholds. A higher AUC PR value means that the model can achieve high precision and high recall at the same time. The AUC PR value is also more suitable for imbalanced data than the AUC ROC value, which measures the trade-off between the true positive rate and the false positive rate. The AUC ROC value can be misleading for imbalanced data, as it can give a high score even if the model has low recall or low precision.
Therefore, option C is the correct answer. Option A is not suitable, as Log loss is a metric that measures the difference between the predicted probabilities and the actual labels, and does not account for the trade-off between precision and recall. Option B is not suitable, as Precision at a Recall value is a metric that measures the precision at a fixed recall level, and does not account for the trade-off between precision and recall at different thresholds. Option D is not suitable, as AUC ROC is a metric that can be misleading for imbalanced data, as explained above.
References:
AutoML Tables documentation
Optimization objectives for binary classification
Precision-Recall Curves: How to Easily Evaluate Machine Learning Models in No Time ROC Curves and Area Under the Curve Explained (video)

NEW QUESTION # 285
You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

• A. Download the data to a Cloud Storage bucket before calling the training job
• B. Enable VPC Service Controls for peering's, and add Vertex Al to a service perimeter
• C. Create a Cloud Run endpoint as a proxy to the data Use Identity and Access Management (1AM) authentication to secure access to the endpoint from the training job.
• D. Configure VPC Peering with Vertex Al and specify the network of the training job

Answer: B

Explanation:
The best option for accessing internal data in the most secure way, while mitigating the risk of data exfiltration, is to enable VPC Service Controls for peerings, and add Vertex AI to a service perimeter. This option allows you to leverage the power and simplicity of VPC Service Controls to isolate and protect your data and services on Google Cloud. VPC Service Controls is a service that can create a secure perimeter around your Google Cloud resources, such as BigQuery, Cloud Storage, and Vertex AI. VPC Service Controls can help you prevent unauthorized access and data exfiltration from your perimeter, and enforce fine- grained access policies based on context and identity. Peerings are connections that can allow traffic to flow between different networks. Peerings can help you connect your Google Cloud network with other Google Cloud networks or external networks, and enable communication between your resources and services. By enabling VPC Service Controls for peerings, you can allow your training code to download internal data by using an API endpoint hosted in your project's network, and restrict the data transfer to only authorized networks and services. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can support various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. By adding Vertex AI to a service perimeter, you can isolate and protect your Vertex AI resources, such as models, endpoints, pi pelines, and feature store, and prevent data exfiltration from your perimeter 1 .
The other options are not as good as option A, for the following reasons:
* Option B: Creating a Cloud Run endpoint as a proxy to the data, and using Identity and Access Management (IAM) authentication to secure access to the endpoint from the training job would require more skills and steps than enabling VPC Service Controls for peerings, and adding Vertex AI to a service perimeter. Cloud Run is a service that can run your stateless containers on a fully managed environment or on your own Google Kubernetes Engine cluster. Cloud Run can help you deploy and scale your containerized applications quickly and easily, and pay only for the resources you use. A Cloud Run endpoint is a URL that can expose your containerized application to the internet or to other Google Cloud services. A Cloud Run endpoint can help you access and invoke your application from anywhere, and handle the load balancing and traffic routing. A proxy is a server that can act as an intermediary between a client and a target server. A proxy can help you modify, filter, or redirect the requests and responses between the client and the target server, and provide additional functionality or security. IAM is a service that can manage access control for Google Cloud resources. IAM can help you define who (identity) has what access (role) to which resource, and enforce the access policies. By creating a Cloud Run endpoint as a proxy to the data, and using IAM authentication to secure access to the endpoint from the training job, you can access internal data by using an API endpoint hosted in your project's network, and restrict the data access to only authorized identities and roles. However, creating a Cloud Run endpoint as a proxy to the data, and using IAM authentication to secure access to the endpoint from the training job would require more skills and steps than enabling VPC Service Controls for peerings, and adding Vertex AI to a service perimeter. You would need to write code, create and configure the Cloud Run endpoint, implement the proxy logic, deploy and monitor the Cloud Run endpoint, and set up the IAM policies. Moreover, this option would not prevent data exfiltration from your network, as the Cloud Run endpoint can be accessed from outside your network 2 .
* Option C: Configuring VPC Peering with Vertex AI and specifying the network of the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could cause errors or poor performance. VPC Peering is a service that can create a peering connection between two VPC networks. VPC Peering can help you connect your Google Cloud network with another Google Cloud network or an external network, and enable communication between your resources and services. By configuring VPC Peering with Vertex AI and specifying the network of the training job, you can allow your training code to access Vertex AI resources, such as models, endpoints, pipelines, and feature store, and use the same network for the training job. However, configuring VPC Peering with Vertex AI and specifying the network of the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could cause errors or poor performance. You would need to write code, create and configure the VPC Peering connection, and specify the network of the training job. Moreover, this option would not isolate and protect your data and services on Go ogle Cloud, as the VPC Peering connection can expose your network to other networks and services 3 .
* Option D: Downloading the data to a Cloud Storage bucket before calling the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could increase the complexity and cost of the data access. Cloud Storage is a service that can store and manage your data on Google Cloud. Cloud Storage can help you upload and organize your data, and track the data versions and metadata. A Cloud Storage bucket is a container that can hold your data on Cloud Storage. A Cloud Storage bucket can help you store and access your data from anywhere, and provide various storage classes and options. By downloading the data to a Cloud Storage bucket before calling the training job, you can access the data from Cloud Storage, and use it as the input for the training job. However, downloading the data to a Cloud Storage bucket before calling the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could increase the complexity and cost of the data access. You would need to write code, create and configure the Cloud Storage bucket, download the data to the Cloud Storage bucket, and call the training job. Moreover, this option would create an intermediate data source on Cloud Storage, which can increase the storage and transfer costs, and expose the data to unauthorized access or data exfiltration 4 .
References:
Preparing for Google Cloud Certification: Machine Learning Engineer , Course 3: Production ML Systems, Week 1: Data Engineering Goo gle Cloud Professional Machine Learning Engineer Exam Guide , Section 1: Framing ML problems, 1.2 Defining data needs Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 2: Data Engineering, Section 2.2: Defining Data Needs VPC Service Controls Cloud Run VPC Peering Cloud Storage

NEW QUESTION # 286
You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data user metadata and game metadat a. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

• A. Read data to a Vertex AI Workbench notebook Use TensorFlow to train a matrix factorization model.
• B. Load the data in BigQuery Use BigQuery ML to tram an Autoencoder model.
• C. Read data to a Vertex Al Workbench notebook Use TensorFlow to train a two-tower model.
• D. Load the data in BigQuery Use BigQuery ML to train a matrix factorization model.

Answer: D

NEW QUESTION # 287
You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?

• A. Store parameters in Cloud SQL store the models' source code in GitHub, and store the models' binaries in Cloud Storage.
• B. Store parameters in Vertex ML Metadata and store the models source code and binaries in GitHub.
• C. Store parameters in Cloud SQL and store the models' source code and binaries in GitHub
• D. Store parameters in Vertex ML Metadata store the models' source code in GitHub and store the models' binaries in Cloud Storage.

Answer: D

Explanation:
To organize the artifacts for dozens of pipelines, you should store the parameters in Vertex ML Metadata, store the models' source code in GitHub, and store the models' binaries in Cloud Storage. This option has the following advantages:
* Vertex ML Metadata is a service that helps you track and manage the metadata of your ML workflows, such as datasets, models, metrics, and parameters1. It can also help you with data lineage, model versioning, and model performance monitoring2.
* GitHub is a popular platform for hosting and collaborating on code repositories. It can help you manage the source code of your models, as well as the configuration files, scripts, and notebooks that are part of your ML pipelines3.
* Cloud Storage is a scalable and durable object storage service that can store any type of data, including model binaries4. It can also integrate with other services, such as Vertex AI, Cloud Functions, and Cloud Run, to enable easy deployment and serving of your models5.
References:
* 1: Introduction to Vertex ML Metadata | Vertex AI | Google Cloud
* 2: Manage metadata for ML workflows | Vertex AI | Google Cloud
* 3: GitHub - Where the world builds software
* 4: Cloud Storage | Google Cloud
* 5: Deploying models | Vertex AI | Google Cloud

NEW QUESTION # 288
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