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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.
Topic 2	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 3	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 4	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
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 data is particularly wide in shape
B. When the model is unable to be parallelized
C. When the entire data can fit on each core
D. When the tuning process in randomized
E. When the data is particularly long in shape

Answer: C

Explanation:
Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.
Reference:
Parallel Computing Concepts

NEW QUESTION # 41
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:

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

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

Answer: D

Explanation:
The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.
Reference
Hyperopt documentation: http://hyperopt.github.io/hyperopt/

NEW QUESTION # 42
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.loc[:,spark_df["price"] > 0]
C. spark_df.filter(col("price") > 0)
D. SELECT * FROM spark_df WHERE price > 0
E. spark_df.loc[spark_df["price"] > 0,:]

Answer: C

Explanation:
To filter rows in a Spark DataFrame based on a condition, you use the filter method along with a column condition. The correct syntax in PySpark to accomplish this task is spark_df.filter(col("price") > 0), which filters the DataFrame to include only those rows where the value in the "price" column is greater than 0. The col function is used to specify column-based operations. The other options provided either do not use correct Spark DataFrame syntax or are intended for different types of data manipulation frameworks like pandas.
Reference:
PySpark DataFrame API documentation (Filtering DataFrames).

NEW QUESTION # 43
A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:

Which of the following changes can the data scientist make to accomplish the task?

A. Replace the random_state=0 argument with random_state=1
B. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
C. Replace the GridSearchCV operation with RandomizedSearchCV
D. Replace the GridSearchCV operation with cross_validate
E. Replace the GridSearchCV operation with ParameterGrid

Answer: C

Explanation:
The user wants to specify a search space for hyperparameters and let the tuning process randomly select values. GridSearchCV systematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming. RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.
Reference
Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

NEW QUESTION # 44
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Logistic regression
B. Least-squares method
C. Singular value decomposition
D. Iterative optimization
E. Spark ML cannot distribute linear regression training

Answer: D

NEW QUESTION # 45
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Reliable Databricks-Machine-Learning-Associate Exam Vce - Exam Dumps Databricks-Machine-Learning-Associate Zip

Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

New Launch Databricks-Machine-Learning-Associate Exam Dumps 2025 - Databricks Databricks-Machine-Learning-Associate Questions

Databricks Certified Machine Learning Associate Exam Sample Questions (Q40-Q45):

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