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Google Professional Machine Learning Engineer Certification Exam is a comprehensive exam that covers a wide range of topics related to machine learning engineering, including data preparation, model development, training, deployment, and monitoring. Professional-Machine-Learning-Engineer exam assesses the candidate's knowledge of the machine learning workflow and their ability to use the Google Cloud Platform to develop and deploy effective machine learning models.
Google Professional Machine Learning Engineer certification exam requires candidates to have a strong understanding of machine learning concepts such as supervised and unsupervised learning, deep learning, natural language processing, and computer vision. Professional-Machine-Learning-Engineer Exam also tests the candidate's ability to work with big data technologies such as Apache Spark, Apache Hadoop, and Google BigQuery.
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Google Professional Machine Learning Engineer Exam is a certification exam that is designed to test the skills and knowledge of professionals who are interested in working with machine learning on the Google Cloud Platform. Professional-Machine-Learning-Engineer Exam is intended for individuals who have a strong understanding of machine learning concepts and experience with implementing and deploying machine learning models in production environments.
NEW QUESTION # 83
You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table How should you perform the inference?
Answer: A
NEW QUESTION # 84
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
Answer: B
Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99
NEW QUESTION # 85
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
Answer: B
Explanation:
The performance of an image classification model can be measured by various metrics, such as accuracy, precision, recall, F1-score, and mean average precision (mAP). These metrics can be calculated based on the confusion matrix, which compares the predicted labels and the true labels of the images1 One of the best ways to monitor the performance of multiple versions of an image classification model on AI Platform is to compare the mean average precision across the models using the Continuous Evaluation feature.
Mean average precision is a metric that summarizes the precision and recall of a model across different confidence thresholds and classes. Mean average precision is especially useful for multi-class and multi-label image classification problems, where the model has to assign one or more labels to each image from a set of possible labels. Mean average precision can range from 0 to 1, where a higher value indicates a better performance2 Continuous Evaluation is a feature of AI Platform that allows you to automatically evaluate the performance of your deployed models using online prediction requests and responses. Continuous Evaluation can help you monitor the quality and consistency of your models over time, and detect any issues or anomalies that may affect the model performance. Continuous Evaluation can also provide various evaluation metrics and visualizations, such as accuracy, precision, recall, F1-score, ROC curve, and confusion matrix, for different types of models, such as classification, regression, and object detection3 To compare the mean average precision across the models using the Continuous Evaluation feature, you need to do the following steps:
* Enable the online prediction logging for each model version that you want to evaluate. This will allow
* AI Platform to collect the prediction requests and responses from your models and store them in BigQuery4
* Create an evaluation job for each model version that you want to evaluate. This will allow AI Platform to compare the predicted labels and the true labels of the images, and calculate the evaluation metrics, such as mean average precision. You need to specify the BigQuery table that contains the prediction logs, the data schema, the label column, and the evaluation interval.
* View the evaluation results for each model version on the AI Platform Models page in the Google Cloud console. You can see the mean average precision and other metrics for each model version over time, and compare them using charts and tables. You can also filter the results by different classes and confidence thresholds.
The other options are not as effective or feasible. Comparing the loss performance for each model on a held-out dataset or on the validation data is not a good idea, as the loss function may not reflect the actual performance of the model on the online prediction data, and may vary depending on the choice of the loss function and the optimization algorithm. Comparing the receiver operating characteristic (ROC) curve for each model using the What-If Tool is not possible, as the What-If Tool does not support image data or multi-class classification problems.
References: 1: Confusion matrix 2: Mean average precision 3: Continuous Evaluation overview 4: Configure online prediction logging : [Create an evaluation job] : [View evaluation results] : [What-If Tool overview]
NEW QUESTION # 86
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook.
You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library.
How should you add the library to your Jupyter kernel?
Answer: A
NEW QUESTION # 87
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?
Answer: B
Explanation:
* Option A is correct because using F-score where recall is weighed more than precision is a suitable metric for binary classification with imbalanced data. F-score is a harmonic mean of precision and recall, which are two metrics that measure the accuracy and completeness of the positive class1. Precision is the fraction of true positives among all predicted positives, while recall is the fraction of true positives among all actual positives1. When the data is imbalanced, the positive class is the minority class, which is usually the class of interest. For example, in this case, the positive class is the images that contain the company's logo, which are rare but important to detect. By weighing recall more than precision, we can emphasize the importance of finding all the positive examples, even if some false positives are included2.
* Option B is incorrect because using RMSE (root mean squared error) is not a valid metric for binary classification with imbalanced data. RMSE is a metric that measures the average magnitude of the errors between the predicted and actual values3. RMSE is suitable for regression problems, where the target variable is continuous, not for classification problems, where the target variable is discrete4.
* Option C is incorrect because using F1 score is not the best metric for binary classification with imbalanced data. F1 score is a special case of F-score where precision and recall are equally weighted1. F1 score is suitable for balanced data, where the positive and negative classes are equally important and frequent5. However, for imbalanced data, the positive class is more important and less frequent than the negative class, so F1 score may not reflect the performance of the model well2.
* Option D is incorrect because using F-score where precision is weighed more than recall is not a good
* metric for binary classification with imbalanced data. By weighing precision more than recall, we can emphasize the importance of minimizing the false positives, even if some true positives are missed2. However, for imbalanced data, the true positives are more important and less frequent than the false positives, so this metric may not reflect the performance of the model well2.
References:
* Precision, recall, and F-measure
* F-score for imbalanced data
* RMSE
* Regression vs classification
* F1 score
* [Imbalanced classification]
* [Binary classification]
NEW QUESTION # 88
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