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

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
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. PyTorch
B. Keras
C. Scikit-learn
D. Spark ML

Answer: D

Explanation:
Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Spark's distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets.
Reference:
Databricks documentation on Spark MLlib: Spark MLlib

NEW QUESTION # 33
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

Answer: B

Explanation:
To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, the RegressionEvaluator class is used. The RegressionEvaluator is specifically designed for regression tasks and can calculate various metrics, including RMSE, based on the columns containing predictions and actual values.
The correct code block to compute RMSE from the preds_df DataFrame is:
regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse" ) rmse = regression_evaluator.evaluate(preds_df) This code creates an instance of RegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions in preds_df and assigns the resulting RMSE value to the rmse variable.
Options A and B incorrectly use BinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly uses BinaryClassificationEvaluator.
Reference:
PySpark ML Documentation

NEW QUESTION # 34
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 requires access to all data at once which cannot happen during parallelization.
B. Gradient boosting is not a linear algebra-based algorithm which is required for 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.

Answer: D

Explanation:
Gradient boosting is fundamentally an iterative algorithm where each new tree is built based on the errors of the previous ones. This sequential dependency makes it difficult to parallelize the training of trees in gradient boosting, as each step relies on the results from the preceding step. Parallelization in this context would undermine the core methodology of the algorithm, which depends on sequentially improving the model's performance with each iteration.
Reference:
Machine Learning Algorithms (Challenges with Parallelizing Gradient Boosting).
Gradient boosting is an ensemble learning technique that builds models in a sequential manner. Each new model corrects the errors made by the previous ones. This sequential dependency means that each iteration requires the results of the previous iteration to make corrections. Here is a step-by-step explanation of why this makes parallelization challenging:
Sequential Nature: Gradient boosting builds one tree at a time. Each tree is trained to correct the residual errors of the previous trees. This requires the model to complete one iteration before starting the next.
Dependence on Previous Iterations: The gradient calculation at each step depends on the predictions made by the previous models. Therefore, the model must wait until the previous tree has been fully trained and evaluated before starting to train the next tree.
Difficulty in Parallelization: Because of this dependency, it is challenging to parallelize the training process. Unlike algorithms that process data independently in each step (e.g., random forests), gradient boosting cannot easily distribute the work across multiple processors or cores for simultaneous execution.
This iterative and dependent nature of the gradient boosting process makes it difficult to parallelize effectively.
Reference
Gradient Boosting Machine Learning Algorithm
Understanding Gradient Boosting Machines

NEW QUESTION # 35
A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

A. They can turn on Databricks Autologging
B. They can start each child run inside the parent run's indented code block using mlflow.start runO
C. They can start each child run with the same experiment ID as the parent run
D. They can specify nested=True when starting the child run for each unique combination of hyperparameter values
E. They can specify nested=True when starting the parent run for the tuning process

Answer: D

Explanation:
To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specify nested=True when starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.
Reference:
MLflow Documentation (Managing Nested Runs).

NEW QUESTION # 36
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 model only needs to be loaded once per executor rather than once per batch during the inference process
B. The model will be limited to a single executor preventing the data from being distributed
C. The data will be distributed across multiple executors during the inference process
D. The data will be limited to a single executor preventing the model from being loaded multiple times

Answer: A

Explanation:
Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.
Reference:
Databricks documentation on pandas UDFs: Pandas UDFs

NEW QUESTION # 37
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Exam Databricks-Machine-Learning-Associate Braindumps - Latest Databricks-Machine-Learning-Associate Exam Pass4sure

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

Exam Databricks-Machine-Learning-Associate Braindumps | Professional Databricks-Machine-Learning-Associate: Databricks Certified Machine Learning Associate Exam 100% Pass

Databricks Certified Machine Learning Associate Exam Sample Questions (Q32-Q37):

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