Databricks Databricks-Machine-Learning-Associate PDF Questions-Turn Your Exam Fear Into Confidence

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

Topic	Details
Topic 1	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
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	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.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
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. Area under the residual operating curve
C. Accuracy
D. Precision
E. RMSE

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 # 23
A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

A. predict(spark_df.columns)
B. mapInPandas(predict(spark_df.columns))
C. predict(Iterator(spark_df))
D. predict(*spark_df.columns)
E. mapInPandas(predict)

Answer: E

Explanation:
To apply the Pandas UDF predict to each record of a Spark DataFrame, you use the mapInPandas method. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predict in this case) to each partition. The correct code completion to execute this is simply mapInPandas(predict), which specifies the UDF to use without additional arguments or incorrect function calls.
Reference:
PySpark DataFrame documentation (Using mapInPandas with UDFs).

NEW QUESTION # 24
Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

A. The vectorized pandas UDFs allow for the use of type hints
B. The vectorized pandas UDFs work on distributed DataFrames
C. The vectorized pandas UDFs process data in memory rather than spilling to disk
D. The vectorized pandas UDFs process data in batches rather than one row at a time
E. The vectorized pandas UDFs allow for pandas API use inside of the function

Answer: D

Explanation:
Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much more efficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.
Reference
PySpark Documentation on UDFs: https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs-a-k-a-vectorized-udfs

NEW QUESTION # 25
A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.
In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

A. When the model is unable to be parallelized
B. When the entire data can fit on each core
C. When the data is particularly long in shape
D. When the tuning process in randomized
E. When the data is particularly wide in shape

Answer: B

Explanation:
Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.
Reference:
Parallel Computing Concepts

NEW QUESTION # 26
A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.
Which of the following terms is used to describe this combination of models?

A. Stacking
B. Ensemble learning
C. Bootstrap aggregation
D. Support vector machines
E. Bucketing

Answer: B

Explanation:
Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.
Reference
Introduction to Ensemble Learning: https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/

NEW QUESTION # 27
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DATABRICKS DATABRICKS-MACHINE-LEARNING-ASSOCIATE PDF QUESTIONS-TURN YOUR EXAM FEAR INTO CONFIDENCE

Databricks Databricks-Machine-Learning-Associate PDF Questions-Turn Your Exam Fear Into Confidence