Databricks-Machine-Learning-Associate Latest Exam Vce | Databricks-Machine-Learning-Associate Valid Dumps Questions

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

Databricks Certified Machine Learning Associate Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
Which of the following lines of code can the data scientist run to accomplish the task?

A. dbutils.data(spark_df).summarize()
B. This task cannot be accomplished in a single line of code.
C. spark_df.describe()
D. dbutils.data.summarize (spark_df)
E. spark_df.summary()

Answer: D

Explanation:
To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility function dbutils.data.summarize can be used. This function provides a comprehensive summary, including visual histograms.
Correct code:
dbutils.data.summarize(spark_df)
Other options like spark_df.describe() and spark_df.summary() provide textual statistical summaries but do not include visual histograms.
Reference:
Databricks Utilities Documentation

NEW QUESTION # 41
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 prep information from the validation sets to the training sets for each model
B. The process will leak data from the training set to the test set during the evaluation phase
C. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode
D. The process will be unable to parallelize tuning due to the distributed nature of pipeline

Answer: C

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 # 42
A new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline's preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day.
Which approach should the data scientist take to complete this task?

A. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.
B. They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.
C. They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes.
D. They can create a new Git repository, import it into Databricks, and copy and paste the existing code from the original repository before making changes.

Answer: A

Explanation:
The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.
Reference:
Databricks documentation on Git integration: Databricks Repos

NEW QUESTION # 43
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. Review the matrix view in the Job's runs
B. Change each Task's setting to use a dedicated cluster
C. Run each notebook interactively
D. Migrate the Job to a Delta Live Tables pipeline

Answer: A

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 # 44
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 model will be limited to a single executor preventing the data from being distributed
C. The data will be distributed across multiple executors during the inference process
D. The data will be limited to a single executor preventing the model from being loaded multiple times

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 # 45
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DATABRICKS-MACHINE-LEARNING-ASSOCIATE LATEST EXAM VCE | DATABRICKS-MACHINE-LEARNING-ASSOCIATE VALID DUMPS QUESTIONS

Databricks-Machine-Learning-Associate Latest Exam Vce | Databricks-Machine-Learning-Associate Valid Dumps Questions