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

Topic	Details
Topic 1	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 2	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 3	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 4	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.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

A. Tree of Parzen Estimators
B. Grid Search
C. Random Search
D. Halving Random Search

Answer: A

Explanation:
Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next.
This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process.
Reference:
Hyperopt and TPE

NEW QUESTION # 61
A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:

The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?

A. It does not impute both the training and test data sets.
B. The inputCols and outputCols need to be exactly the same.
C. The fit method needs to be called instead of transform.
D. It does not fit the imputer on the data to create an ImputerModel.

Answer: D

Explanation:
In the provided code block, the Imputer object is created but not fitted on the data to generate an ImputerModel. The transform method is being called directly on the Imputer object, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first.
Corrected code:
imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df) # Fit the imputer to the data imputed_features_df = imputer_model.transform(features_df) # Transform the data using the fitted imputer Reference:
PySpark ML Documentation

NEW QUESTION # 62
A data scientist is working with a feature set with the following schema:

The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each feature.
Which of the following lists all of the columns in the feature set that need to be imputed using the most common value of the column?

A. customer_id
B. units
C. spend
D. loyalty_tier
E. customer_id, loyalty_tier

Answer: D

Explanation:
For the feature set schema provided, the columns that need to be imputed using the most common value (mode) are typically the categorical columns. In this case, loyalty_tier is the only categorical column that should be imputed using the most common value. customer_id is a unique identifier and should not be imputed, while spend and units are numerical columns that should typically be imputed using the mean or median values, not the mode.
Reference:
Databricks documentation on missing value imputation: Handling Missing Data If you need any further clarification or additional questions answered, please let me know!

NEW QUESTION # 63
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. Increase num_evals to 100
D. Change tpe.suggest to random.suggest

Answer: C

Explanation:
To improve the likelihood of a more accurate model, the data scientist can increase num_evals to 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.
Reference:
Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

NEW QUESTION # 64
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. Least-squares method
B. Logistic regression
C. Singular value decomposition
D. Spark ML cannot distribute linear regression training
E. Iterative optimization

Answer: E

Explanation:
For large datasets with many variables, Spark ML distributes the training of a linear regression model using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient Descent or L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) to iteratively minimize the loss function. These iterative methods are suitable for distributed computing environments and can handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing parallel updates.
Reference:
Spark MLlib Documentation (Linear Regression with Iterative Optimization).

NEW QUESTION # 65
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WE PROVIDE 100% PREMIUM DATABRICKS DATABRICKS-MACHINE-LEARNING-ASSOCIATE EXAM QUESTIONS

We provide 100% premium Databricks Databricks-Machine-Learning-Associate exam questions