1z0-1096-23 無料問題集「Oracle Machine Learning using Autonomous Database 2023 Associate」

（A）K-means clustering

（B）Random Forest

（C）Support Vector Machines

（D）Linear Regression

（E）Association rule

（A）They require known outcomes to guide the learning process.

（B）They predict numeric values along a continuum.

（C）They extract rules using unsupervised learning.

（D）They assign cases to target categories.

（A）Association Rules

（B）Anomaly Detection

（C）Clustering

（D）Regression

（A）It is used to extract meaningful insights from raw data to improve data operational efficiency.

（B）There is no previously known result to guide the algorithm in building the model.

（C）It does not specify a target, it can be applied to a population of interest.

（D）The learning process is directed by a previously known dependent attribute or target.

（E）It generally results in predictive models.

（A）Export the notebook in .json format and have your colleague import this notebook.

（B）Save the notebook in the .csv format and have your colleague import this notebook.

（C）Save the notebook as a shared template.

（D）Make the notebook public.

（A）Model Selection

（B）Algorithm Selection

（C）Feature Selection

（D）Hyperparameter Tuning

（E）Adaptive Sampling

（A）You can share notebooks with Import/Export operations.

（B）Within notebook paragraphs you can switch between data views of tables, pie charts, bar charts, line plots and scatter plots.

（C）You can set the output format in SQL paragraphs of a notebook.

（D）When visualizing a 1 million row database data using the built-in Zeppelin visualizers, OML will by default display the results on the entire table.