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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 (Q45-Q50):

NEW QUESTION # 45
In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

A. When the features contain no missing no values
B. When the features contain no outliers
C. When the features are of the boolean type
D. When the features contain a lot of extreme outliers
E. When the features are of the categorical type

Answer: D

Explanation:
Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.
Reference:
Data Imputation Techniques (Dealing with Outliers).

NEW QUESTION # 46
A data scientist is using the following code block to tune hyperparameters for a machine learning model:

Which change can they make the above code block to improve the likelihood of a more accurate model?

A. Change sparkTrials() to Trials()
B. Increase num_evals to 100
C. Change tpe.suggest to random.suggest
D. Change fmin() to fmax()

Answer: B

Explanation:
To improve the likelihood of a more accurate model, the data scientist can increase num_evals to 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.
Reference:
Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

NEW QUESTION # 47
A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.
As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

A. Remove the algo=tpe.suggest argument
B. Change SparkTrials() to Trials()
C. Remove the trials=trials argument
D. Reduce num_evals to be less than 10
E. Change fmin() to fmax()

Answer: B

Explanation:
The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.
Reference
Hyperopt documentation: http://hyperopt.github.io/hyperopt/

NEW QUESTION # 48
A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

A. They can refactor their notebook to use the Scala Dataset API.
B. They can refactor their notebook to process the data in parallel.
C. They can refactor their notebook to utilize the pandas API on Spark.
D. They can refactor their notebook to use the PySpark DataFrame API.
E. They can refactor their notebook to use Spark SQL.

Answer: C

Explanation:
The data scientist can refactor their notebook to utilize the pandas API on Spark (now known as pandas on Spark, formerly Koalas). This allows for the least amount of changes to the existing pandas-based code while scaling to handle big data using Spark's distributed computing capabilities. pandas on Spark provides a similar API to pandas, making the transition smoother and faster compared to completely rewriting the code to use PySpark DataFrame API, Scala Dataset API, or Spark SQL.
Reference:
Databricks documentation on pandas API on Spark (formerly Koalas).

NEW QUESTION # 49
A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

A. They can check the Databricks Runtime ML box when creating their clusters.
B. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
C. They can set the runtime-version variable in their Spark session to "ml".
D. They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.

Answer: B

Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

NEW QUESTION # 50
......

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