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

Topic	Details
Topic 1	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 2	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
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 (Q74-Q79):

NEW QUESTION # 74
A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

A. import pyspark.pandas as ps
df = ps.to_pandas(spark_df)
B. spark_df.to_sql()
C. import pyspark.pandas as ps
df = ps.DataFrame(spark_df)
D. spark_df.to_pandas()
E. import pandas as pd
df = pd.DataFrame(spark_df)

Answer: C

Explanation:
To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed to pandas_api_on_spark) module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.
Reference
Pandas API on Spark Documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

NEW QUESTION # 75
A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?

A. When the new solution requires that each model computes a prediction for every record
B. When the new solution requires the use of fewer feature variables than the original model
C. When the new solution's models have an average size that is larger than the size of the original model
D. When the new solution's models have an average latency that is larger than the size of the original model
E. When the new solution requires if-else logic determining which model to use to compute each prediction

Answer: A

Explanation:
If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.
In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.
Reference:
Model Ensemble Techniques

NEW QUESTION # 76
A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the path model_uri for the DataFrame batch_df.
batch_df has the following schema:
customer_id STRING
The machine learning engineer runs the following code block to perform inference on batch_df using the linear regression model at model_uri:

In which situation will the machine learning engineer's code block perform the desired inference?

A. When all of the features used by the model at model_uri are in a single Feature Store table
B. When the model at model_uri only uses customer_id as a feature
C. When all of the features used by the model at model_uri are in a Spark DataFrame in the PySpark
D. This code block will not perform the desired inference in any situation.
E. When the Feature Store feature set was logged with the model at model_uri

Answer: E

Explanation:
The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.
Reference:
Databricks documentation on Feature Store: Feature Store in Databricks

NEW QUESTION # 77
Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

A. PyTorch
B. Spark ML
C. Scikit-learn
D. Keras

Answer: B

Explanation:
Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Spark's distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets.
Reference:
Databricks documentation on Spark MLlib: Spark MLlib

NEW QUESTION # 78
A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?

A. None of these compute tools support this task
B. Standard cluster
C. Single Node cluster
D. SQL Warehouse

Answer: B

Explanation:
For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.
Reference:
Databricks documentation on clusters: Clusters in Databricks

NEW QUESTION # 79
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