DATABRICKS-MACHINE-LEARNING-ASSOCIATE EXAM DUMPS WITH GUARANTEED SUCCESS

Databricks-Machine-Learning-Associate EXAM DUMPS WITH GUARANTEED SUCCESS

Databricks-Machine-Learning-Associate EXAM DUMPS WITH GUARANTEED SUCCESS

Blog Article

Tags: Test Databricks-Machine-Learning-Associate Pass4sure, Databricks-Machine-Learning-Associate Hottest Certification, Dump Databricks-Machine-Learning-Associate Torrent, Valid Databricks-Machine-Learning-Associate Exam Question, Vce Databricks-Machine-Learning-Associate Format

DOWNLOAD the newest BraindumpsPass Databricks-Machine-Learning-Associate PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1uxUfPQYrc87RpPx8ducrjlZgUBhuXDDi

We will provide 24-hour online service for you on our Databricks-Machine-Learning-Associate exam questios. If you can’t decide what kind of Databricks-Machine-Learning-Associate exam practice to choose, you shall have a chance to consult us, You can ask the questions that you want to know about our Databricks-Machine-Learning-Associate Study Guide, we will listen to you carefully, according to your Databricks-Machine-Learning-Associate exam, we guarantee to meet your requirements without wasting your purchasing funds.

Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 2
  • 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 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.

>> Test Databricks-Machine-Learning-Associate Pass4sure <<

Pass Guaranteed 2025 Databricks Databricks-Machine-Learning-Associate Useful Test Pass4sure

Our experts have worked hard for several years to formulate Databricks-Machine-Learning-Associate exam braindumps for all examiners. Our Databricks-Machine-Learning-Associate study materials not only target but also cover all knowledge points. And our practice materials also have a statistical analysis function to help you find out the deficiency in the learning process of Databricks-Machine-Learning-Associate practice materials, so that you can strengthen the training for weak links. In this way, you can more confident for your success since you have improved your ability.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of including pipeline as the estimator in the cross-validation process rather than rfr as the estimator?

  • A. The process will leak data from the training set to the test set during the evaluation phase
  • B. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode
  • C. The process will be unable to parallelize tuning due to the distributed nature of pipeline
  • D. The process will leak data prep information from the validation sets to the training sets for each model

Answer: B

Explanation:
Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.
If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.
Reference:
Databricks documentation on cross-validation: Cross Validation


NEW QUESTION # 22
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

  • A. String indexing
  • B. Target encoding
  • C. One-hot encoding
  • D. Categorical
  • E. Leave-one-out encoding

Answer: C

Explanation:
The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.
Reference:
Feature Engineering Techniques (One-Hot Encoding).


NEW QUESTION # 23
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 tpe.suggest to random.suggest
  • C. Change fmin() to fmax()
  • D. Change sparkTrials() to Trials()

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 # 24
A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.
Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

  • A. Spark SQL
  • B. pandas API on Spark
  • C. PySpark DataFrame API
  • D. Feature Store

Answer: B

Explanation:
The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Spark's distributed computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark


NEW QUESTION # 25
A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:

The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?

  • A. The inputCols and outputCols need to be exactly the same.
  • B. The fit method needs to be called instead of transform.
  • C. It does not impute both the training and test data sets.
  • D. It does not fit the imputer on the data to create an ImputerModel.

Answer: D

Explanation:
In the provided code block, the Imputer object is created but not fitted on the data to generate an ImputerModel. The transform method is being called directly on the Imputer object, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first.
Corrected code:
imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df) # Fit the imputer to the data imputed_features_df = imputer_model.transform(features_df) # Transform the data using the fitted imputer Reference:
PySpark ML Documentation


NEW QUESTION # 26
......

Can you imagine that you only need to review twenty hours to successfully obtain the Databricks-Machine-Learning-Associate certification? Can you imagine that you don’t have to stay up late to learn and get your boss’s favor? With Databricks-Machine-Learning-Associate study quiz, passing exams is no longer a dream. If you are an office worker, Databricks-Machine-Learning-Associate Preparation questions can help you make better use of the scattered time to review. Just visit our website and try our Databricks-Machine-Learning-Associate exam questions, then you will find what you need.

Databricks-Machine-Learning-Associate Hottest Certification: https://www.braindumpspass.com/Databricks/Databricks-Machine-Learning-Associate-practice-exam-dumps.html

BTW, DOWNLOAD part of BraindumpsPass Databricks-Machine-Learning-Associate dumps from Cloud Storage: https://drive.google.com/open?id=1uxUfPQYrc87RpPx8ducrjlZgUBhuXDDi

Report this page