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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	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 3	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 4	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster.
Which of the following approaches will guarantee a reproducible training and test set for each model?

A. Manually configure the cluster
B. Manually partition the input data
C. Set a speed in the data splitting operation
D. Write out the split data sets to persistent storage

Answer: D

Explanation:
To ensure reproducible training and test sets, writing the split data sets to persistent storage is a reliable approach. This allows you to consistently load the same training and test data for each model run, regardless of cluster reconfiguration or other changes in the environment.
Correct approach:
Split the data.
Write the split data to persistent storage (e.g., HDFS, S3).
Load the data from storage for each model training session.
train_df, test_df = spark_df.randomSplit([0.8, 0.2], seed=42) train_df.write.parquet("path/to/train_df.parquet") test_df.write.parquet("path/to/test_df.parquet") # Later, load the data train_df = spark.read.parquet("path/to/train_df.parquet") test_df = spark.read.parquet("path/to/test_df.parquet") Reference:
Spark DataFrameWriter Documentation

NEW QUESTION # 48
A data scientist has replaced missing values in their feature set with each respective feature variable's median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set?

A. Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them
B. Remove all feature variables that originally contained missing values from the feature set
C. Impute the missing values using each respective feature variable's mean value instead of the median value
D. Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed
E. Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing

Answer: D

Explanation:
By creating a binary feature variable for each feature with missing values to indicate whether a value has been imputed, the data scientist can preserve information about the original state of the data. This approach maintains the integrity of the dataset by marking which values are original and which are synthetic (imputed). Here are the steps to implement this approach:
Identify Missing Values: Determine which features contain missing values.
Impute Missing Values: Continue with median imputation or choose another method (mean, mode, regression, etc.) to fill missing values.
Create Indicator Variables: For each feature that had missing values, add a new binary feature. This feature should be '1' if the original value was missing and imputed, and '0' otherwise.
Data Integration: Integrate these new binary features into the existing dataset. This maintains a record of where data imputation occurred, allowing models to potentially weight these observations differently.
Model Adjustment: Adjust machine learning models to account for these new features, which might involve considering interactions between these binary indicators and other features.
Reference
"Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari (O'Reilly Media, 2018), especially the sections on handling missing data.
Scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html

NEW QUESTION # 49
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 process data in memory rather than spilling to disk
C. The vectorized pandas UDFs work on distributed DataFrames
D. The vectorized pandas UDFs allow for pandas API use inside of the function
E. The vectorized pandas UDFs process data in batches rather than one row at a time

Answer: E

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 # 50
A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.
Which of the following feature engineering tasks will be the least efficient to distribute?

A. Target encoding categorical features
B. Imputing missing feature values with the true median
C. One-hot encoding categorical features
D. Creating binary indicator features for missing values
E. Imputing missing feature values with the mean

Answer: B

Explanation:
Among the options listed, calculating the true median for imputing missing feature values is the least efficient to distribute. This is because the true median requires knowledge of the entire data distribution, which can be computationally expensive in a distributed environment. Unlike mean or mode, finding the median requires sorting the data or maintaining a full distribution, which is more intensive and often requires shuffling the data across partitions.
Reference
Challenges in parallel processing and distributed computing for data aggregation like median calculation: https://www.apache.org

NEW QUESTION # 51
Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

A. Click the "Source" link in the row corresponding to the run in the MLflow experiment page
B. Open the MLmodel artifact in the MLflow run paqe
C. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
D. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page

Answer: A

Explanation:
To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.
Reference:
MLflow Documentation (Viewing Run Sources and Notebooks).

NEW QUESTION # 52
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FREE PDF QUIZ DATABRICKS - DATABRICKS-MACHINE-LEARNING-ASSOCIATE ACCURATE EXAM ASSESSMENT

Free PDF Quiz Databricks - Databricks-Machine-Learning-Associate Accurate Exam Assessment