Latest Databricks-Machine-Learning-Associate Exam Questions - Databricks-Machine-Learning-Associate Valid Test Preparation

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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	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 3	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Topic 4	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?

A. Recall
B. RMSE
C. Area under the residual operating curve
D. Accuracy
E. Precision

Answer: A

Explanation:
When the goal is to maximize the identification of positive cases in a classification task, the metric of interest is Recall. Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified by the model (i.e., the true positive rate). It is crucial for scenarios where missing a positive case (false negative) has serious implications, such as in medical diagnostics. The other metrics like Precision, RMSE, and Accuracy serve different aspects of performance measurement and are not specifically focused on maximizing the detection of positive cases alone.
Reference:
Classification Metrics in Machine Learning (Understanding Recall).

NEW QUESTION # 43
A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.
The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

A. They do not need to make any changes
B. They need to convert the features column to be a vector
C. They need to call the transform method on train df
D. They need to split the features column out into one column for each feature
E. They need to utilize a Pipeline to fit the model

Answer: B

Explanation:
In Spark ML, the linear regression model expects the feature column to be a vector type. However, if the features column in the DataFrame train_df is not already in this format (such as being a column of type UDT or a non-vectorized type), the engineer needs to convert it to a vector column using a transformer like VectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.
Reference
Spark MLlib documentation for LinearRegression: https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

NEW QUESTION # 44
A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.
They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

A. Add a random_state argument to the RandomForestRegressor operation
B. Remove the mean operation that is wrapping the cross_val_score operation
C. Replace the fmin operation with the fmax operation
D. Replace the r2 return value with -r2
E. Add test set validation process

Answer: D

Explanation:
When using the Hyperopt library with fmin, the goal is to find the minimum of the objective function. Since you are using cross_val_score to calculate the R2 score which is a measure of the proportion of the variance for a dependent variable that's explained by an independent variable(s) in a regression model, higher values are better. However, fmin seeks to minimize the objective function, so to align with fmin's goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2, fmin is effectively maximizing the R2 score, which can lead to a more accurate model.
Reference
Hyperopt Documentation: http://hyperopt.github.io/hyperopt/
Scikit-Learn documentation on model evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html

NEW QUESTION # 45
A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?

A. Migrate the Job to a Delta Live Tables pipeline
B. Run each notebook interactively
C. Change each Task's setting to use a dedicated cluster
D. Review the matrix view in the Job's runs

Answer: D

Explanation:
To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach is more efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.
Reference:
Databricks documentation on Jobs: Jobs in Databricks

NEW QUESTION # 46
A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

A. The model only needs to be loaded once per executor rather than once per batch during the inference process
B. The data will be distributed across multiple executors during the inference process
C. The data will be limited to a single executor preventing the model from being loaded multiple times
D. The model will be limited to a single executor preventing the data from being distributed

Answer: A

Explanation:
Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.
Reference:
Databricks documentation on pandas UDFs: Pandas UDFs

NEW QUESTION # 47
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LATEST DATABRICKS-MACHINE-LEARNING-ASSOCIATE EXAM QUESTIONS - DATABRICKS-MACHINE-LEARNING-ASSOCIATE VALID TEST PREPARATION

Latest Databricks-Machine-Learning-Associate Exam Questions - Databricks-Machine-Learning-Associate Valid Test Preparation