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Certified Machine Learning Associate by Databricks - Page 1 | ExamCademy

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

Which of the following statements describes a Spark ML estimator?

A An estimator is a hyperparameter grid that can be used to train a model
B An estimator chains multiple algorithms together to specify an ML workflow
C An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
D An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
E An estimator is an evaluation tool to assess to the quality of a model

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.
As a result, they have the following code block:

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Which of the following changes do they need to make to the above code block in order to accomplish the task?

A Change SparkTrials() to Trials()
B Reduce num_evals to be less than 10
C Change fmin() to fmax()
D Remove the trials=trials argument
E Remove the algo=tpe.suggest argument

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 discount is less than or equal 0.
Which of the following code blocks will accomplish this task?

A spark_df.loc[:,spark_df["discount"] <= 0]
B spark_df[spark_df["discount"] <= 0]
C spark_df.filter (col("discount") <= 0)
D spark_df.loc(spark_df["discount"] <= 0, :]

A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?

A
B
C
D

A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

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They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

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Which of the following lines of code can be used to complete the code block to successfully complete the task?

A predict(*spark_df.columns)
B mapInPandas(predict)
C predict(Iterator(spark_df))
D mapInPandas(predict(spark_df.columns))
E predict(spark_df.columns)

A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically.
Which of the following lines of code will return the metadata description?

A There is no way to return the metadata description programmatically.
B fs.create_training_set("new_table")
C fs.get_table("new_table").description
D fs.get_table("new_table").load_df()
E fs.get_table("new_table")

A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?

A RMSE
B Precision
C Area under the residual operating curve
D Accuracy
E Recall

In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

A When the features are of the categorical type
B When the features are of the boolean type
C When the features contain a lot of extreme outliers
D When the features contain no outliers
E When the features contain no missing values

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

A One-hot encoding is not supported by most machine learning libraries.
B One-hot encoding is dependent on the target variable’s values which differ for each application.
C One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
D One-hot encoding is not a common strategy for representing categorical feature variables numerically.
E One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.
Which of the following feature engineering tasks will be the least efficient to distribute?

A One-hot encoding categorical features
B Target encoding categorical features
C Imputing missing feature values with the mean
D Imputing missing feature values with the true median
E Creating binary indicator features for missing values

A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

A Model tuning
B Model evaluation
C Model deployment
D Exploratory data analysis

A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

A import pyspark.pandas as psdf = ps.DataFrame(spark_df)
B import pyspark.pandas as psdf = ps.to_pandas(spark_df)
C spark_df.to_sql()
D import pandas as pddf = pd.DataFrame(spark_df)
E spark_df.to_pandas()

Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

A The vectorized pandas UDFs allow for the use of type hints
B The vectorized pandas UDFs process data in batches rather than one row at a time
C The vectorized pandas UDFs allow for pandas API use inside of the function
D The vectorized pandas UDFs work on distributed DataFrames
E The vectorized pandas UDFs process data in memory rather than spilling to disk

A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical.
Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?

A Spark ML decision trees test every feature variable in the splitting algorithm
B Spark ML decision trees automatically prune overfit trees
C Spark ML decision trees test more split candidates in the splitting algorithm
D Spark ML decision trees test a random sample of feature variables in the splitting algorithm
E Spark ML decision trees test binned features values as representative split candidates

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