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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 (Q25-Q30):
NEW QUESTION # 25
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. Increase num_evals to 100
- B. Change fmin() to fmax()
- C. Change sparkTrials() to Trials()
- D. Change tpe.suggest to random.suggest
Answer: A
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 # 26
Which of the following machine learning algorithms typically uses bagging?
- A. K-means
- B. Linear regression
- C. Random forest
- D. Decision tree
- E. Gradient boosted trees
Answer: C
Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.
Reference:
Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).
NEW QUESTION # 27
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. Change SparkTrials() to Trials()
- B. Change fmin() to fmax()
- C. Reduce num_evals to be less than 10
- D. Remove the algo=tpe.suggest argument
- E. Remove the trials=trials argument
Answer: A
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 # 28
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?
- A. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
- B. pandas API on Spark DataFrames are more performant than Spark DataFrames
- C. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
- D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
- E. pandas API on Spark DataFrames are unrelated to Spark DataFrames
Answer: A
Explanation:
Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.
Reference
pandas API on Spark documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html
NEW QUESTION # 29
A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?
- A. None of these compute tools support this task
- B. SQL Warehouse
- C. Standard cluster
- D. Single Node cluster
Answer: C
Explanation:
For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.
Reference:
Databricks documentation on clusters: Clusters in Databricks
NEW QUESTION # 30
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