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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	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.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column discount is less than or equal 0.
Which of the following code blocks will accomplish this task?

A. spark_df.loc(spark_df["discount"] <= 0, :]
B. spark_df.loc[:,spark_df["discount"] <= 0]
C. spark_df[spark_df["discount"] <= 0]
D. spark_df.filter (col("discount") <= 0)

Answer: D

Explanation:
To filter rows in a Spark DataFrame based on a condition, the filter method is used. In this case, the condition is that the value in the "discount" column should be less than or equal to 0. The correct syntax uses the filter method along with the col function from pyspark.sql.functions.
Correct code:
from pyspark.sql.functions import col filtered_df = spark_df.filter(col("discount") <= 0) Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of the col function.
Reference:
PySpark SQL Documentation

NEW QUESTION # 66
A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

A. Bias is avoidable when using a train-validation split
B. A holdout set is not necessary when using a train-validation split
C. Fewer models need to be trained when using a train-validation split
D. Fewer hyperparameter values need to be tested when using a train-validation split
E. Reproducibility is achievable when using a train-validation split

Answer: C

NEW QUESTION # 67
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. RMSE
B. Accuracy
C. Recall
D. Precision
E. Area under the residual operating curve

Answer: C

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 # 68
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. Run each notebook interactively
B. Change each Task's setting to use a dedicated cluster
C. Migrate the Job to a Delta Live Tables pipeline
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 # 69
A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

A. The experiment page in the Experiments observatory
B. The model page in the MLflow Model Registry
C. The home page of the MLflow Model Registry
D. The model version page in the MLflow Model Registry

Answer: D

Explanation:
The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.
Reference
Databricks documentation on MLflow Model Registry: https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

NEW QUESTION # 70
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USE ACTUAL DATABRICKS DATABRICKS-MACHINE-LEARNING-ASSOCIATE TO PREVENT MENTAL HASSLE

Use Actual Databricks Databricks-Machine-Learning-Associate to Prevent Mental Hassle