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Associate exam? Get complete syllabus details by clicking
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ML Associate Exam
Format
Exam Name: Databricks Certified
Machine Learning Associate
Exam Code: Machine Learning Associate
Duration: 90 mins
Number of Questions: 45
Passing Score: 70%
Practice Exams: Databricks Machine
Learning Associate Certification Practice
Exam
Recommended Training: Scalable
Machine Learning with Apache Spark
Machine Learning Associate
Questions
Databricks Machine
Learning Associate
Get a glimpse of the actual
exam questions with the
help of these sample
questions.
Q1. Which of the following steps are necessary to commit changes from a
Databricks Repo to an external Git provider? (Select two)
Options:
a) Merge changes to the master branch in the external Git provider
b) Use Databricks notebooks to push changes
c) Stage and commit changes in the Databricks workspace
d) Pull requests from the Databricks workspace to the Git provider
Answer:
b) Use Databricks notebooks to push changes
c) Stage and commit changes in the Databricks workspace
Q2. How can you identify the best run using the MLflow Client API?
Options:
a) By manually reviewing each run's metrics.
b) Utilizing the search_runs function with a specific metric sort order.
c) Comparing run IDs manually for performance metrics.
d) Using a custom Python script outside of MLflow.
Answer:
b) Utilizing the search_runs function with a specific
metric sort order.
Q3. Which of the following are key components of ML workflows in Databricks?
Options:
a) Data ingestion
b) Model serving
c) Feature extraction
d) Manual model tuning
Answer:
a) Data ingestion
b) Model serving
c) Feature extraction
Q4. When AutoML explores the key attributes of a dataset, which of the following
elements does it typically not assess?
Options:
a) The dataset's memory footprint.
b) The potential impact of outliers on model performance.
c) The balance or imbalance of classes in classification tasks.
d) The encryption level of the dataset.
Answer:
d) The encryption level of the dataset.
Q5. Where can you find the code that was executed with a run in the MLflow UI?
Options:
a) In the run's metadata section.
b) Inside the associated Git repository.
c) Under the "Code" tab in the run's details page.
d) It is not possible to view the executed code in the MLflow UI.
Answer:
c) Under the "Code" tab in the run's details page.
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