Professional-Machine-Learning-Engineer Prüfungsfragen, Professional-Machine-Learning-Engineer Testking

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Unsere Google Professional-Machine-Learning-Engineer Prüfungsunterlage (Google Professional Machine Learning Engineer) enthalten alle echten, originalen und richtigen Fragen und Antworten. Die Abdeckungsrate unserer Google Professional-Machine-Learning-Engineer Unterlagen (Fragen und Antworten) (Google Professional Machine Learning Engineer) ist normalerweise mehr als 98%.

Die Google Professional Machine Learning Engineer-Zertifizierungsprüfung eine herausfordernde, aber lohnende Erfahrung für Fachleute, die ihre Karriere im Bereich des maschinellen Lernens vorantreiben möchten. Durch gründliche Vorbereitung und Demonstration Ihrer Kompetenz in Konzepten und Techniken des maschinellen Lernens können Sie diese renommierte Zertifizierung erreichen und neue Karrieremöglichkeiten eröffnen.

Die Prüfung zum Professional Machine Learning Engineer ist eine leistungsorientierte Bewertung, die die Fähigkeit des Kandidaten bewertet, mithilfe von Machine-Learning-Techniken reale Probleme zu lösen. Die Prüfung besteht aus einer Reihe praktischer Aufgaben, bei denen der Kandidat sein Verständnis für verschiedene Machine-Learning-Konzepte und seine Fähigkeit, sie in praktischen Szenarien anzuwenden, unter Beweis stellen muss. Die Prüfung wird online abgehalten und kann von überall auf der Welt abgelegt werden.

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Professional-Machine-Learning-Engineer Testking, Professional-Machine-Learning-Engineer Prüfungsunterlagen

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Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q257-Q262):

257. Frage
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

Antwort: A


258. Frage
You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence.
You want to use Vertex Al to understand your model's results.
What should you do?

Antwort: D

Begründung:
Vertex Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services1. With Vertex Explainable AI, you can generate feature-based explanations that show how much each input feature contributed to the model's prediction2. This can help you debug and improve your model performance, and build confidence in your model's behavior. Feature-based explanations are supported for custom image classification models deployed on Vertex AI Prediction3. References:
* Explainable AI | Google Cloud
* Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
* Supported model types for feature-based explanations | Vertex AI | Google Cloud


259. Frage
You built a deep learning-based image classification model by using on-premises dat a. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

Antwort: A


260. Frage
You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers

Antwort: C,E

Begründung:
The best options for adjusting the training parameters in AutoML to improve model performance are to decrease the score threshold and add more positive examples to the training set. These options can help increase the detection rate of fraudulent transactions, which is the priority for this use case. The score threshold is a parameter that determines the minimum probability score that a prediction must have to be classified as positive. Decreasing the score threshold can increase the recall of the model, which is the proportion of actual positive cases that are correctly identified. Increasing the recall can help reduce the number of false negatives, which are fraudulent transactions that aremissed by the model. However, decreasing the score threshold can also decrease the precision of the model, which is the proportion of positive predictions that are actually correct. Decreasing the precision can increase the number of false positives, which are legitimate transactions that are flagged as fraudulent by the model. Therefore, there is a trade-off between recall and precision, and the optimal score threshold depends on the business objective and the cost of errors1.
Adding more positive examples to the training set can help balance the data distribution and improve the model performance. Positive examples are the instances that belong to the target class, which in this case are fraudulent transactions. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Fraudulent transactions are usually rare and imbalanced compared to legitimate transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class. Adding more positive examples can help the model learn more features and patterns of the fraudulent transactions, and increase the detection rate2.
The other options are not as good as options B and C, for the following reasons:
* Option A: Increasing the score threshold would decrease the detection rate of fraudulent transactions, which is the opposite of the desired outcome. Increasing the score threshold would decrease the recall of the model, which is the proportion of actual positive cases that are correctly identified. Decreasing the recall would increase the number of false negatives, which are fraudulent transactions that are missed by the model. Increasing the score threshold would increase the precision of the model, which is the proportion of positive predictions that are actually correct. Increasing the precision would decrease the number of false positives, which are legitimate transactions that are flagged as fraudulent by the model. However, in this use case, the cost of false negatives is much higher than the cost of false positives, so increasing the score threshold is not a good option1.
* Option D: Adding more negative examples to the training set would not improve the model performance, and could worsen the data imbalance. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Legitimate transactions are usually abundant and dominant compared to fraudulent transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class. Adding more negative examples would exacerbate this problem, and decrease the detection rate of the fraudulent transactions2.
* Option E: Reducing the maximum number of node hours for training would not improve the model performance, and could limit the model optimization. Node hours are the units of computation that are used to train an AutoML model. The maximum number of node hours is a parameter that determines the upper limit of node hours that can be used for training. Reducing the maximum number of node hours would reduce the training time and cost, but also the model quality and accuracy. Reducing the maximum number of node hours would limit the number of iterations, trials, and evaluations that the model can perform, and prevent the model from finding the optimal hyperparameters and architecture3.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week
4: Evaluation
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Developing high-quality ML models, 2.2 Handling imbalanced data
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4:
* Low-code ML Solutions, Section 4.3: AutoML
* Understanding the score threshold slider
* Handling imbalanced data sets in machine learning
* AutoML Vision pricing


261. Frage
You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

Antwort: A

Begründung:
A random forest is an ensemble learning method that consists of many decision trees. It can be used for both regression and classification tasks. A random forest classification model can predict the probability of churn for each customer by assigning them to different classes, such as high-risk, medium-risk, or low-risk. A random forest model can also generate feature importances, which measure how much each feature contributes to the prediction. Feature importances can help interpret the model and understand what factors influence customer churn. Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. You can use Vertex AI Workbench to build a random forest classification model in Python, using libraries such as scikit-learn or TensorFlow. You can also configure the model to generate feature importances after the model is trained, and visualize them using plots or tables. This solution can help you build an interpretable model for customer churn prediction, and use the results to design marketing campaigns that target at-risk customers. Reference:
Random Forests | scikit-learn
Vertex AI Workbench | Google Cloud
Interpreting Random Forests | Towards Data Science


262. Frage
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