Databricks-Machine-Learning-Associate 試験問題を無料オンラインアクセス

Which of the following machine learning algorithms typically uses bagging?

• A.IGradient boosted trees
• B.Decision tree
• C.K-means
• D.Random forest

A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

• A.They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
• B.They can check the Databricks Runtime ML box when creating their clusters.
• C.They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.
• D.They can set the runtime-version variable in their Spark session to "ml".

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

• A.PvTorch
• B.pandas
• C.Scikit-learn
• D.Spark ML
• E.Keras

A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.
Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

• A.PySpark DataFrame API
• B.Feature Store
• C.Spark SQL
• D.pandas API on Spark

A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column price is greater than 0.
Which of the following code blocks will accomplish this task?

• A.spark_df.filter(col("price") > 0)
• B.spark_df.loc[:,spark_df["price"] > 0]
• C.SELECT * FROM spark_df WHERE price > 0
• D.spark_df[spark_df["price"] > 0]
• E.spark_df.loc[spark_df["price"] > 0,:]