Databricks Machine Learning
Associate Exam Questions
Databricks Certified Machine Learning Associate Certification - Correct Answer
✔✔The Databricks Certified Machine Learning Associate certification exam assesses
an individual's ability to use Databricks to perform basic machine learning tasks.
This includes an ability to understand and use Databricks Machine Learning and its
capabilities like AutoML, Feature Store, and select capabilities of MLflow. It also
assesses the ability to make correct decisions in machine learning workflows and
implement those workflows using Spark ML. Finally, the ability to understand
advanced characteristics of scaling machine learning models is assessed.
Individuals who pass this certification exam can be expected to complete basic
machine learning tasks using Databricks and its associated tools.
About the Databricks Machine Learning Associate Exam - Correct Answer ✔✔●
Number of items: 45 multiple-choice questions● Time limit: 90 minutes●
Registration fee: $200● Languages: English● Delivery method: Online Proctored●
Type: Proctored certification● Test aides: None allowed.● Prerequisite: None
required; course attendance and six months of hands-on experience in Databricks is
highly recommended● Validity: 2 years● Recommended experience: 6+ months of
hands-on experience performing the machine learning tasks outlined in the exam
guide
Exam Topics - Correct Answer ✔✔Section 1: Databricks Machine Learning 29%
Section 2: ML Workflows 29%
Section 3: Spark ML 33%
Section 4: Scaling ML Models 9%
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.fminB.SparkTrialsC.quniformD.search_spaceE.objective_function - Correct Answer
✔✔Answer: B
An organization is developing a feature repository and is electing to one-hot encode
all categorical feature variables. A data scientist suggests that the categorical
feature variables should not be one-hot encoded within the feature repository.Which
of the following explanations justifies this suggestion?A.One-hot encoding is a
potentially problematic categorical variable strategy for some machine learning
algorithms.B.One-hot encoding is dependent on the target variable's values which
differ for each apaplication.C.One-hot encoding is computationally intensive and
should only be performed on small samples of training sets for individual machine
learning problems.D.One-hot encoding is not a common strategy for representing
categorical feature variables numerically. - Correct Answer ✔✔Answer: A
