BigQuery Flashcards
Is Logistic Regression built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
What is AutoML used for?
supervised ML service that builds and deploys classification and regression models on tabular data at high speed and scale.
What is Deep neural network used for?
creating TensorFlow-based deep neural networks for classification and regression models.
Is Random forest built-in to BigQuery or externally trained in Vertex AI?
Trained in Vertex AI
What is K-means clustering used for?
data segmentation. For example, this model identifies customer segments. K-means is an unsupervised learning technique, so model training doesn’t require labels or split data for training or evaluation.
Is Boosted Tree built-in to BigQuery or externally trained in Vertex AI?
Trained in Vertex AI
What is Boosted Tree used for?
creating classification and regression models that are based on XGBoost.
Is Contribution Analysis built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
What is Random forest used for?
constructing multiple learning method decision trees for classification, regression, and other tasks at training time.
What is Principal component analysis (PCA) used for?
the process of computing the principal components and using them to perform a change of basis on the data. It’s commonly used for dimensionality reduction by projecting each data point onto only the first few principal components to obtain lower-dimensional data while preserving as much of the data’s variation as possible.
What is Time series used for?
performing time series forecasts. You can use this feature to create millions of time series models and use them for forecasting. The model automatically handles anomalies, seasonality, and holidays.
Is Autoencoder built-in to BigQuery or externally trained in Vertex AI?
Trained in Vertex AI
Is Time Series built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
What is Linear regression used for?
Predicting the value of a numerical metric for new data by using a model trained on similar remote data. Labels are real-valued, meaning they cannot be positive infinity or negative infinity or a NaN.
Is Wide & Deep built-in to BigQuery or externally trained in Vertex AI?
Trained in Vertex AI
Is AutoML built-in to BigQuery or externally trained in Vertex AI?
Trained in Vertex AI
Language used in BigQuery
SQL
Is Linear Regression built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
Is Deep neural network (DNN) built-in to BigQuery or externally trained in Vertex AI?
Trained in Vertex AI
What is Autoencoder used for?
creating TensorFlow-based models with the support of sparse data representations. You can use the models in BigQuery ML for tasks such as unsupervised anomaly detection and non-linear dimensionality reduction.
What is Logistic regression used for?
the classification of two or more possible values such as whether an input is low-value, medium-value, or high-value. Labels can have up to 50 unique values.
What is Contribution Analysis used for?
Determining the effect of one or more dimensions on the value for a given metric. For example, seeing the effect of store location and sales date on store revenue.
Is Matrix Factorisation built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
What is Matrix factorization used for?
creating product recommendation systems. You can create product recommendations using historical customer behavior, transactions, and product ratings, and then use those recommendations for personalized customer experiences.
Do you need to move and format data for Python-based ML frameworks?
No, BigQuery brings ML to the Data, in the SQL database.
Is K-means Clustering built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
Is Principal Component Analysis built-in to BigQuery or externally trained in Vertex AI?
Built in to BigQuery
What is Wide & Deep used for?
generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Feature Selection and Engineering can be done using what keyword?
TRANSFORM
What can the TRANSFORM clause do?
Feature selection AND feature engineering
