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NEW QUESTION 27
Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading. Which approach should you use?

  • A. Manually label a few hundred articles, and then train an SVM classifier based on the manually classified articles that categorizes additional articles into their respective categories.
  • B. Encode all articles into vectors using word2vec, and build a model that returns articles based on vector similarity.
  • C. Create a collaborative filtering system that recommends articles to a user based on the user’s past behavior.
  • D. Build a logistic regression model for each user that predicts whether an article should be recommended to a user.

Answer: C

 

NEW QUESTION 28
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

  • A. Decrease the range of floating-point values
  • B. Set the early stopping parameter to TRUE
  • C. Decrease the maximum number of trials during subsequent training phases.
  • D. Change the search algorithm from Bayesian search to random search.
  • E. Decrease the number of parallel trials

Answer: C,D

 

NEW QUESTION 29
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

  • A. Manage all relational entities in the Hive Metastore.
  • B. Store your tf.logging data in BigQuery.
  • C. Manage your ML workflows with Vertex ML Metadata.
  • D. Store all ML metadata in Google Cloud’s operations suite.

Answer: D

 

NEW QUESTION 30
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?

  • A. An optimization objective that maximizes the Precision at a Recall value of 0.50
  • B. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
  • C. An optimization objective that minimizes Log loss
  • D. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value

Answer: D

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 31
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

  • A. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
  • B. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
  • C. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
  • D. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.

Answer: C

 

NEW QUESTION 32
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