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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	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
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 (Q29-Q34):

NEW QUESTION # 29
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. PvTorch
B. pandas
C. Keras
D. Spark ML
E. Scikit-learn

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 # 30
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 be refit one more per cross-validation fold
B. The cross-validation process will no longer be
C. The cross-validation process will no longer be reproducible
D. The feature engineering stages will be computed using validation data
E. The model will take longer to train for each unique combination of hvperparameter values

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 # 31
A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?

Answer: D

Explanation:
To find the run_id of the run with the best root-mean-square error (RMSE) in an MLflow experiment, the correct line of code to use is:
mlflow.search_runs( experiment_id, order_by=["metrics.rmse"] )["run_id"][0] This line of code searches the runs in the specified experiment, orders them by the RMSE metric in ascending order (the lower the RMSE, the better), and retrieves the run_id of the best-performing run. Option C correctly represents this logic.
Reference
MLflow documentation on tracking experiments: https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.search_runs

NEW QUESTION # 32
A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?

A. When the new solution requires if-else logic determining which model to use to compute each prediction
B. When the new solution's models have an average latency that is larger than the size of the original model
C. When the new solution requires that each model computes a prediction for every record
D. When the new solution's models have an average size that is larger than the size of the original model
E. When the new solution requires the use of fewer feature variables than the original model

Answer: C

Explanation:
If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.
In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.
Reference:
Model Ensemble Techniques

NEW QUESTION # 33
A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical.
Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?

A. Spark ML decision trees test binned features values as representative split candidates
B. Spark ML decision trees test every feature variable in the splitting algorithm
C. Spark ML decision trees automatically prune overfit trees
D. Spark ML decision trees test more split candidates in the splitting algorithm
E. Spark ML decision trees test a random sample of feature variables in the splitting algorithm

Answer: A

Explanation:
One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.
Reference:
Spark MLlib Documentation (Decision Trees and Quantile Binning).

NEW QUESTION # 34
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2025 UNLIMITED DATABRICKS-MACHINE-LEARNING-ASSOCIATE EXAM PRACTICE PASS CERTIFY | VALID TEST DATABRICKS-MACHINE-LEARNING-ASSOCIATE CRAM: DATABRICKS CERTIFIED MACHINE LEARNING ASSOCIATE EXAM

2025 Unlimited Databricks-Machine-Learning-Associate Exam Practice Pass Certify | Valid Test Databricks-Machine-Learning-Associate Cram: Databricks Certified Machine Learning Associate Exam