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 Databricks-Machine-Learning-Associate Dumps with Practice Exam Questions Answers

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Last Update: Jun 19, 2024

Databricks-Machine-Learning-Associate Question Includes: Single Choice Questions: 74,

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Databricks-Machine-Learning-Associate Questions and Answers

Question # 1

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model in parallel. They elect to use the Hyperopt library to facilitate this process.

Which of the following Hyperopt tools provides the ability to optimize hyperparameters in parallel?

A.

fmin

B.

SparkTrials

C.

quniform

D.

search_space

E.

objective_function

Question # 2

A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.

In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

A.

When the tuning process in randomized

B.

When the entire data can fit on each core

C.

When the model is unable to be parallelized

D.

When the data is particularly long in shape

E.

When the data is particularly wide in shape

Question # 3

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

Question # 4

A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.

Which of the following lines of code can the data scientist run to accomplish the task?

A.

spark_df.describe()

B.

dbutils.data(spark_df).summarize()

C.

This task cannot be accomplished in a single line of code.

D.

spark_df.summary()

E.

dbutils.data.summarize (spark_df)

Question # 5

A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult.

Which of the following describes why?

A.

Gradient boosting is not a linear algebra-based algorithm which is required for parallelization

B.

Gradient boosting requires access to all data at once which cannot happen during parallelization.

C.

Gradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.

D.

Gradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.