Professional-Machine-Learning-Engineer Prüfung - Professional-Machine-Learning-Engineer Zertifizierung

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Um für die Zertifizierung zum Professional Machine Learning Engineer berechtigt zu sein, müssen Kandidaten eine solide Grundlage in Maschinenlernkonzepten, Programmiersprachen wie Python und Cloud-Computing-Technologien haben. Darüber hinaus müssen die Kandidaten Erfahrung in der Arbeit mit Google Cloud Platform und ihren verschiedenen Maschinenlern-Tools wie TensorFlow, AutoML und BigQuery haben. Es wird empfohlen, dass Kandidaten mindestens drei Jahre Erfahrung im Bereich des Maschinenlernens haben, bevor sie diese Zertifizierung anstreben.

Die Google Professional Machine Learning Engineer Prüfung ist eine Zertifizierung, die von Google Cloud angeboten wird und dazu dient, die Fähigkeiten und Expertise von Personen im Bereich des maschinellen Lernens zu validieren. Diese Zertifizierung richtet sich an Fachleute, die Erfahrung in der Entwicklung und Bereitstellung von maschinellen Lernmodellen unter Verwendung von Google Cloud-Technologien haben.

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Professional-Machine-Learning-Engineer Zertifizierung - Professional-Machine-Learning-Engineer Zertifikatsdemo

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Das Erlangen der Google-Professional-Machine-Learning-Engineer-Zertifizierung zeigt Arbeitgebern und Kunden, dass Sie die Fähigkeiten und Kenntnisse haben, die für die Gestaltung und Implementierung effektiver Machine-Learning-Lösungen auf der Google Cloud Platform erforderlich sind. Es ist eine wertvolle Referenz für Datenwissenschaftler, Softwareingenieure und andere Fachleute, die ihr Können im Bereich Machine Learning und Cloud Computing entwickeln möchten.

Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q173-Q178):

173. Frage
You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure.
You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?

A. The model with the highest precision where recall is greater than 0.5.
B. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
C. The model with the highest recall where precision is greater than 0.5.
D. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5

Antwort: C

Begründung:
In predictive maintenance, the goal is to identify which machines are likely to fail soon, so that the repair crew can fix them before they break. In this context, it is important to prioritize detection, while also ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure.
Recall is a metric that measures the proportion of actual positive observations that are correctly predicted as such by the model. In this case, recall is a good metric to use because it measures how well the model is able to identify the machines that are likely to fail soon.
Precision is a metric that measures the proportion of positive predictions that are actually true. In this case, precision is also important because it measures how many of the machines that the model predicts will fail soon, actually do fail soon.
By combining these two metrics, you can ensure that your model is able to identify the machines that are likely to fail soon with a high degree of accuracy. In this case, the model with the highest recall where precision is greater than 0.5 will be the best model, as it will have a high ability to identify the machines that are likely to fail soon and also it will have a high degree of accuracy.
Reference:
Recall and Precision
Predictive Maintenance
Metrics for classification

174. Frage
You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

A. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
B. Containerize the PySpark transformation step, and add it to your pipeline.
C. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
D. Remove the data transformation step from your pipeline.

Antwort: A

Begründung:
The best option for parametrizing the model training in Kubeflow Pipelines is to add a ContainerOp to the pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage. This option has the following advantages:
* It allows the data transformation to be performed as part of the Kubeflow Pipeline, which can ensure the consistency and reproducibility of the data processing and the model training. By adding a ContainerOp to the pipeline, you can define the parameters and the logic of the data transformation step, and integrate it with the other steps of the pipeline, such as the model training and evaluation.
* It leverages the scalability and performance of Dataproc, which is a fully managed service that runs Apache Spark and Apache Hadoop clusters on Google Cloud. By spinning a Dataproc cluster, you can run the PySpark transformation on the Parquet files stored in the Hive table, and take advantage of the parallelism and speed of Spark. Dataproc also supports various features and integrations, such as autoscaling, preemptible VMs, and connectors to other Google Cloud services, that can optimize the data processing and reduce the cost.
* It simplifies the data storage and access, as the transformed data is saved in Cloud Storage, which is a scalable, durable, and secure object storage service. By saving the transformed data in Cloud Storage,
* you can avoid the overhead and complexity of managing the data in the Hive table or the Parquet files.
Moreover, you can easily access the transformed data from Cloud Storage, using various tools and frameworks, such as TensorFlow, BigQuery, or Vertex AI.
The other options are less optimal for the following reasons:
* Option A: Removing the data transformation step from the pipeline eliminates the parametrization of the model training, as the data processing and the model training are decoupled and independent. This option requires running the PySpark transformation separately from the Kubeflow Pipeline, which can introduce inconsistency and unreproducibility in the data processing and the model training. Moreover, this option requires managing the data in the Hive table or the Parquet files, which can be cumbersome and inefficient.
* Option B: Containerizing the PySpark transformation step, and adding it to the pipeline introduces additional complexity and overhead. This option requires creating and maintaining a Docker image that can run the PySpark transformation, which can be challenging and time-consuming. Moreover, this option requires running the PySpark transformation on a single container, which can be slow and inefficient, as it does not leverage the parallelism and performance of Spark.
* Option D: Deploying Apache Spark at a separate node pool in a Google Kubernetes Engine cluster, and adding a ContainerOp to the pipeline that invokes a corresponding transformation job for this Spark instance introduces additional complexity and cost. This option requires creating and managing a separate node pool in a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires deploying and running Apache Spark on the node pool, which can be tedious and costly, as it requires configuring and maintaining the Spark cluster, and paying for the node pool usage.

175. Frage
You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition, model type, color, and engine/battery efficiency. The data is updated every night. Car dealerships will use the model to determine appropriate car prices. You created a Vertex AI pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost.
What should you do?

A. Compare the results to the evaluation results from a previous run. If the performance improved deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring.
When the model monitoring threshold is triggered redeploy the pipeline.
B. Compare the training and evaluation losses of the current run. If the losses are similar, deploy the model to a Vertex AI endpoint. Configure a cron job to redeploy the pipeline every night.
C. Compare the training and evaluation losses of the current run. If the losses are similar, deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered redeploy the pipeline.
D. Compare the results to the evaluation results from a previous run. If the performance improved deploy the model to a Vertex AI endpoint. Configure a cron job to redeploy the pipeline every night.

Antwort: C

176. Frage
You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the - raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al's hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

A. Run one hypertuning job with training_method as the hyperparameter for 50 trials Select the architecture with the lowest training loss. and further hypertune It and its corresponding hyperparameters for 50 trials
B. Run two separate hypertuning jobs. a linear regression job for 50 trials, and a DNN job for 50 trials Compare their final performance on a common validation set. and select the set of hyperparameters with the least training loss
C. Run one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.
D. Run one hypertuning job for 100 trials. Set num hidden_layers as a conditional hypetparameter based on its parent hyperparameter training_mothod. and set learning rate as a non-conditional hyperparameter

Antwort: C

177. Frage
Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

A. Initialize the model with pre-trained weights in all layers including the last fully connected layer.
B. Initialize the model with random weights in all layers and replace the last fully connected layer.
C. Initialize the model with random weights in all layers including the last fully connected layer.
D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Antwort: D

Begründung:
Explanation/Reference:

178. Frage
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