Certification Databricks-Machine-Learning-Associate Test Answers & Latest Databricks-Machine-Learning-Associate Exam Testking

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

Topic	Details
Topic 1	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
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	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.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

A. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page
B. Open the MLmodel artifact in the MLflow run paqe
C. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
D. Click the "Source" link in the row corresponding to the run in the MLflow experiment page

Answer: D

Explanation:
To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.
Reference:
MLflow Documentation (Viewing Run Sources and Notebooks).

NEW QUESTION # 22
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

A. The model will take longer to train for each unique combination of hvperparameter values
B. The cross-validation process will no longer be
C. The model will be refit one more per cross-validation fold
D. The feature engineering stages will be computed using validation data
E. The cross-validation process will no longer be reproducible

Answer: D

Explanation:
If the model object is passed to the estimator parameter of CrossValidator and the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.
Reference:
Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

NEW QUESTION # 23
A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the path model_uri for the DataFrame batch_df.
batch_df has the following schema:
customer_id STRING
The machine learning engineer runs the following code block to perform inference on batch_df using the linear regression model at model_uri:

In which situation will the machine learning engineer's code block perform the desired inference?

A. When the Feature Store feature set was logged with the model at model_uri
B. When the model at model_uri only uses customer_id as a feature
C. When all of the features used by the model at model_uri are in a Spark DataFrame in the PySpark
D. When all of the features used by the model at model_uri are in a single Feature Store table
E. This code block will not perform the desired inference in any situation.

Answer: A

Explanation:
The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.
Reference:
Databricks documentation on Feature Store: Feature Store in Databricks

NEW QUESTION # 24
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. SparkTrials
B. fmin
C. search_space
D. quniform
E. objective_function

Answer: A

Explanation:
The SparkTrials class in the Hyperopt library allows for parallel hyperparameter optimization on a Spark cluster. This enables efficient tuning of hyperparameters by distributing the optimization process across multiple nodes in a cluster.
from hyperopt import fmin, tpe, hp, SparkTrials search_space = { 'x': hp.uniform('x', 0, 1), 'y': hp.uniform('y', 0, 1) } def objective(params): return params['x'] ** 2 + params['y'] ** 2 spark_trials = SparkTrials(parallelism=4) best = fmin(fn=objective, space=search_space, algo=tpe.suggest, max_evals=100, trials=spark_trials) Reference:
Hyperopt Documentation

NEW QUESTION # 25
A machine learning engineer wants to parallelize the inference of group-specific models using the Pandas Function API. They have developed the apply_model function that will look up and load the correct model for each group, and they want to apply it to each group of DataFrame df.
They have written the following incomplete code block:

Which piece of code can be used to fill in the above blank to complete the task?

A. mapInPandas
B. groupedApplyInPandas
C. predict
D. applyInPandas

Answer: D

Explanation:
To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use the applyInPandas function. This function allows you to apply a Python function on each group of a DataFrame and return a DataFrame, leveraging the power of pandas UDFs (user-defined functions) for better performance.
prediction_df = ( df.groupby("device_id") .applyInPandas(apply_model, schema=apply_return_schema) ) In this code:
groupby("device_id"): Groups the DataFrame by the "device_id" column.
applyInPandas(apply_model, schema=apply_return_schema): Applies the apply_model function to each group and specifies the schema of the return DataFrame.
Reference:
PySpark Pandas UDFs Documentation

NEW QUESTION # 26
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CERTIFICATION DATABRICKS-MACHINE-LEARNING-ASSOCIATE TEST ANSWERS & LATEST DATABRICKS-MACHINE-LEARNING-ASSOCIATE EXAM TESTKING

Certification Databricks-Machine-Learning-Associate Test Answers & Latest Databricks-Machine-Learning-Associate Exam Testking