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Google Professional Machine Learning Engineer Certification Exam is a highly prestigious certification offered by Google. It is designed for individuals who wish to showcase their expertise in the field of machine learning and demonstrate their ability to design, build, and deploy highly scalable and reliable machine learning models. Google Professional Machine Learning Engineer certification exam tests candidates on a variety of topics related to machine learning, including data preprocessing, feature engineering, model selection and evaluation, and deployment and monitoring of machine learning models.
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Google Professional Machine Learning Engineer certification exam is considered one of the most challenging and prestigious certifications in the field of machine learning. Achieving this certification demonstrates that the candidate has the knowledge, skills, and expertise to design and implement machine learning solutions that meet the highest standards of quality and performance. Google Professional Machine Learning Engineer certification is a clear indication of the candidate's ability to leverage machine learning to solve complex business problems and drive innovation in the industry.
NEW QUESTION # 145
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?
Answer: B
Explanation:
https://stats.stackexchange.com/questions/262616/roc-vs-precision-recall-curves-on-imbalanced-dataset
https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc
https://icaiit.org/proceedings/6th_ICAIIT/1_3Fayzrakhmanov.pdf The problem of fraudulent transactions detection, which is an imbalanced classification problem (most transactions are not fraudulent), you want to maximize both precision and recall; so the area under the PR curve. As a matter of fact, the question asks you to focus on detecting fraudulent transactions (maximize true positive rate, a.k.a. Recall) while minimizing false positives (a.k.a. maximizing Precision). Another way to see it is this: for imbalanced problems like this one you'll get a lot of true negatives even from a bad model (it's easy to guess a transaction as "non-fraudulent" because most of them are!), and with high TN the ROC curve goes high fast, which would be misleading. So you wanna avoid dealing with true negatives in your evaluation, which is precisely what the PR curve allows you to do.
NEW QUESTION # 146
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
Answer: A,D
NEW QUESTION # 147
You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes?
Answer: D
NEW QUESTION # 148
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
Answer: C
NEW QUESTION # 149
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?
Answer: C
NEW QUESTION # 150
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