Databricks-Machine-Learning-Associate無料問題集「Databricks Certified Machine Learning Associate」

質問 1

A data scientist is using the following code block to tune hyperparameters for a machine learning model:

Which change can they make the above code block to improve the likelihood of a more accurate model?

（A）Change sparkTrials() to Trials()

（B）Change fmin() to fmax()

（C）Change tpe.suggest to random.suggest

（D）Increase num_evals to 100

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

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）spark_df.to_pandas()

（B）import pyspark.pandas as ps
df = ps.DataFrame(spark_df)

（C）import pyspark.pandas as ps
df = ps.to_pandas(spark_df)

（D）import pandas as pd
df = pd.DataFrame(spark_df)

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

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 a potentially problematic categorical variable strategy for some machine learning algorithms.

（B）One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

（C）One-hot encoding is dependent on the target variable's values which differ for each apaplication.

（D）One-hot encoding is not a common strategy for representing categorical feature variables numerically.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

（A）Enable autoscaling clusters

（B）Implement MLflow Experiment Tracking

（C）Parallelize with Hyperopt

（D）Scale up with Spark ML

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:
* Hyperparameter 1: [2, 5, 10]
* Hyperparameter 2: [50, 100]
Which of the following represents the number of machine learning models that can be trained in parallel during this process?

（A）3

（B）5

（C）18

（D）6

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

Which statement describes a Spark ML transformer?

（A）A transformer is an algorithm which can transform one DataFrame into another DataFrame

（B）A transformer is a hyperparameter grid that can be used to train a model

（C）A transformer chains multiple algorithms together to transform an ML workflow

（D）A transformer is a learning algorithm that can use a DataFrame to train a model

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

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）SELECT * FROM spark_df WHERE price > 0

（B）spark_df[spark_df["price"] > 0]

（C）spark_df.loc[spark_df["price"] > 0,:]

（D）spark_df.loc[:,spark_df["price"] > 0]

（E）spark_df.filter(col("price") > 0)

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

（A）The data will be limited to a single executor preventing the model from being loaded multiple times

（B）The data will be distributed across multiple executors during the inference process

（C）The model only needs to be loaded once per executor rather than once per batch during the inference process

（D）The model will be limited to a single executor preventing the data from being distributed

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Databricks-Machine-Learning-Associate 無料問題集「Databricks Certified Machine Learning Associate」

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