Pass Guaranteed Quiz Databricks - Databricks-Machine-Learning-Associate - Unparalleled Flexible Databricks Certified Machine Learning Associate Exam Learning Mode

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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	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 3	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 4	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
A data scientist has replaced missing values in their feature set with each respective feature variable's median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set?

A. Remove all feature variables that originally contained missing values from the feature set
B. Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them
C. Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing
D. Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed
E. Impute the missing values using each respective feature variable's mean value instead of the median value

Answer: D

Explanation:
By creating a binary feature variable for each feature with missing values to indicate whether a value has been imputed, the data scientist can preserve information about the original state of the data. This approach maintains the integrity of the dataset by marking which values are original and which are synthetic (imputed). Here are the steps to implement this approach:
Identify Missing Values: Determine which features contain missing values.
Impute Missing Values: Continue with median imputation or choose another method (mean, mode, regression, etc.) to fill missing values.
Create Indicator Variables: For each feature that had missing values, add a new binary feature. This feature should be '1' if the original value was missing and imputed, and '0' otherwise.
Data Integration: Integrate these new binary features into the existing dataset. This maintains a record of where data imputation occurred, allowing models to potentially weight these observations differently.
Model Adjustment: Adjust machine learning models to account for these new features, which might involve considering interactions between these binary indicators and other features.
Reference
"Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari (O'Reilly Media, 2018), especially the sections on handling missing data.
Scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html

NEW QUESTION # 66
A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?

A. Standard cluster
B. None of these compute tools support this task
C. Single Node cluster
D. SQL Warehouse

Answer: A

Explanation:
For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.
Reference:
Databricks documentation on clusters: Clusters in Databricks

NEW QUESTION # 67
Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

A. TrainValidationSplitModel
B. DataFrame.where
C. DataFrame.randomSplit
D. TrainValidationSplit
E. CrossValidator

Answer: C

Explanation:
The correct method to randomly split a Spark DataFrame into training and test sets is by using the randomSplit method. This method allows you to specify the proportions for the split as a list of weights and returns multiple DataFrames according to those weights. This is directly intended for splitting DataFrames randomly and is the appropriate choice for preparing data for training and testing in machine learning workflows.
Reference:
Apache Spark DataFrame API documentation (DataFrame Operations: randomSplit).

NEW QUESTION # 68
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. pandas
B. Keras
C. Scikit-learn
D. Spark ML
E. PvTorch

Answer: D

Explanation:
Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.
Reference:
Spark MLlib documentation (Feature Engineering with Spark ML).

NEW QUESTION # 69
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. Remove the algo=tpe.suggest argument
B. Change SparkTrials() to Trials()
C. Reduce num_evals to be less than 10
D. Change fmin() to fmax()
E. Remove the trials=trials argument

Answer: B

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 # 70
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PASS GUARANTEED QUIZ DATABRICKS - DATABRICKS-MACHINE-LEARNING-ASSOCIATE - UNPARALLELED FLEXIBLE DATABRICKS CERTIFIED MACHINE LEARNING ASSOCIATE EXAM LEARNING MODE

Pass Guaranteed Quiz Databricks - Databricks-Machine-Learning-Associate - Unparalleled Flexible Databricks Certified Machine Learning Associate Exam Learning Mode