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| Section | Objectives |
|---|---|
| Topic 1: ML pipeline automation and orchestration | - Pipeline design
|
| Topic 2: Designing ML solutions | - ML architecture design
|
| Topic 3: Deployment and operations | - Monitoring and maintenance
|
| Topic 4: Data preparation and processing | - Feature engineering
|
| Topic 5: ML model development | - Model training and tuning
|
>> Professional-Machine-Learning-Engineer Übungsmaterialien <<
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94. Frage
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?
Antwort: A
Begründung:
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, pipelines, and feature store, and prevent data exfiltration from your perimeter1.
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 network2.
* 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 Google Cloud, as the VPC Peering connection can expose your network to other networks and services3.
* 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 exfiltration4.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 1: Data Engineering
* Google 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
95. Frage
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests
24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?
Antwort: A
Begründung:
The best option for deploying a scikit-learn classification model to production is to deploy an online Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to leverage the power and scalability of Google Cloud to serve requests 24/7 and handle millions of requests per second. 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. An online prediction endpoint consists of one or more replicas, which are copies of the model that run on virtual machines. The max replica count is a parameter that determines the maximum number of replicas that can be created for the endpoint. By setting the max replica count to 100, you can enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases. This can help minimize the cost of deployment, as you only pay for the resources that you use. Moreover, you can use the autoscaling algorithm option to optimize the scaling behavior of the endpoint based on the latency and utilization metrics 1 .
The other options are not as good as option B, for the following reasons:
* Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second. Setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases. Moreover, se tting the max replica count to 1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which can increase the cost of deployment, as you pay for the resources that you do not use 1 .
* Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second, and would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also in crease the cost of deployment, as GPUs are more expensive than CPUs. Moreover, setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions whe n the traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic decreases 1
. Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration 2 .
* Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 100 would be able to serve requests 24/7 and handle millions of requests per second, but it would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases, which can help minimize the cost of deployment. However , scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration 2 . Therefore, using GPUs for scikit-learn models would be unnecessary and wasteful.
References:
Preparing for Google 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 Online prediction Scaling online prediction scikit-learn FAQ
96. Frage
You have created a Vertex Al pipeline that automates custom model training You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?
Antwort: B
Begründung:
Vertex AI Experiments is a managed service that allows you to track, compare, and manage experiments with Vertex AI. You can use Vertex AI Experiments to record the parameters, metrics, and artifacts of each pipeline run, and compare them in a graphical interface. Vertex AI TensorBoard is a tool that lets you visualize the metrics of your models, such as accuracy, loss, and learning curves. By logging metrics to Vertex ML Metadata and using Vertex AI Experiments and TensorBoard, you can easily collaborate with your team and find the best model configuration for your problem. References: Vertex AI Pipelines: Metrics visualization and run comparison using the KFP SDK, Track, compare, manage experiments with Vertex AI Experiments, Vertex AI Pipelines
97. Frage
You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min- max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?
Antwort: B
Begründung:
The best option for configuring the workflow is to add the transformations as a preprocessing layer in the TensorFlow models. This option allows you to leverage the power and simplicity of TensorFlow to preprocess and transform the data with simple Python code. TensorFlow is a framework for building and training machine learning models. TensorFlow provides various tools and libraries for data analysis and machine learning. A preprocessing layer is a type of layer in TensorFlow that can perform data preprocessing and feature engineering operations on the input data. A preprocessing layer can help you customize the data transformation and preprocessing logic, and handle complex or non-standard data formats. A preprocessing layer can also help you minimize the preprocessing time, cost, and development effort, as you only need to write a few lines of code to implement the preprocessing layer, and you do not need to create any intermediate data sources or pipelines. By adding the transformations as a preprocessing layer in the TensorFlow models, you can use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales1.
The other options are not as good as option C, for the following reasons:
* Option A: Writing the transformations into Spark that uses the spark-bigquery-connector and using Dataproc to preprocess the data would require more skills and steps than using a preprocessing layer in TensorFlow. Spark is a framework for distributed data processing and machine learning. Spark can read and write data from BigQuery by using the spark-bigquery-connector, which is a library that allows Spark to communicate with BigQuery. Dataproc is a service that can create and manage Spark clusters on Google Cloud. Dataproc can help you run Spark jobs on Google Cloud, and scale the clusters according to the workload. However, writing the transformations into Spark that uses the spark- bigquery-connector and using Dataproc to preprocess the data would require more skills and steps than using a preprocessing layer in TensorFlow. You would need to write code, create and configure the Spark cluster, install and import the spark-bigquery-connector, load and preprocess the data, and write the data back to BigQuery. Moreover, this option would create an intermediate data source in BigQuery, which can increase the storage and computation costs2.
* Option B: Writing SQL queries to transform the data in-place in BigQuery would not allow you to use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. BigQuery is a service that can perform data analysis and machine learning by using SQL queries. BigQuery can perform data transformation and preprocessing by using SQL functions and clauses, such as MIN, MAX, CASE, and TRANSFORM. BigQuery can also perform machine learning by using BigQuery ML, which is a feature that can create and train machine learning models by using SQL queries. However, writing SQL queries to transform the data in-place in BigQuery would not allow you to use Vertex AI's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. Vertex AI's custom training service is a service that can run your custom machine learning code on Vertex AI. Vertex AI's custom training service can support various machine learning frameworks, such as TensorFlow, PyTorch, and scikit- learn. Vertex AI's custom training service cannot support SQL queries, as SQL is not a machine learning framework. Therefore, if you want to use Vertex AI's custom training service, you cannot use SQL queries to transform the data in-place in BigQuery3.
* Option D: Creating a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery would require more skills and steps than using a preprocessing layer in TensorFlow. Dataflow is a service that can create and run data processing and machine learning pipelines on Google Cloud. Dataflow can read and write data from BigQuery by using the BigQueryIO connector, which is a library that allows Dataflow to communicate with BigQuery. Dataflow can perform data transformation and preprocessing by using Apache Beam, which is a framework for distributed data processing and machine learning. However, creating a Dataflow pipeline that uses the BigQueryIO connector to ingest the data, process it, and write it back to BigQuery would require more skills and steps than using a preprocessing layer in TensorFlow. You would need to write code, create and configure the Dataflow pipeline, install and import the BigQueryIO connector, load and preprocess the data, and write the data back to BigQuery. Moreover, this option would create an intermediate data source in BigQuery, which can increase the storage and computation costs4.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 2: Serving ML Predictions
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Developing ML models, 2.1 Developing ML models by using TensorFlow
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4:
Developing ML Models, Section 4.1: Developing ML Models by Using TensorFlow
* TensorFlow Preprocessing Layers
* Spark and BigQuery
* Dataproc
* BigQuery ML
* Dataflow and BigQuery
* Apache Beam
98. Frage
You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage.
You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?
Antwort: C
Begründung:
According to the web search results, the TFRecord format is a recommended way to store large amounts of data efficiently and improve the performance of the data input pipeline123. The TFRecord format is a binary format that can be compressed and serialized, which reduces the I/O overhead and the memory footprint of the data1. The tf.data API provides tools to create and read TFRecord files easily1.
The other options are not as effective as option A. Option B would reduce the amount of data available for training and might affect the model accuracy. Option C would still require reading from a single CSV file at a time, which might not utilize the full bandwidth of the remote storage. Option D would only affect the order of the data elements, not the speed of reading them.
99. Frage
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