Databricks-Machine-Learning-Associate Exam Fragen - Databricks-Machine-Learning-Associate Antworten

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Databricks Databricks-Machine-Learning-Associate Prüfungsplan:

Thema Einzelheiten
Thema 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.
Thema 2
  • Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Thema 3
  • ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.
Thema 4
  • Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

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Databricks-Machine-Learning-Associate Antworten, Databricks-Machine-Learning-Associate Echte Fragen

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Databricks Certified Machine Learning Associate Exam Databricks-Machine-Learning-Associate Prüfungsfragen mit Lösungen (Q53-Q58):

53. Frage
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 pandas as pd
    df = pd.DataFrame(spark_df)
  • D. import pyspark.pandas as ps
    df = ps.DataFrame(spark_df)
  • E. spark_df.to_pandas()

Antwort: D

Begründung:
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


54. Frage
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. import pandas as pd
    df = pd.DataFrame(spark_df)
  • C. import pyspark.pandas as ps
    df = ps.DataFrame(spark_df)
  • D. spark_df.to_pandas()

Antwort: C

Begründung:
To use the pandas API on Spark, the data scientist can run the following code block:
import pyspark.pandas as ps df = ps.DataFrame(spark_df)
This code imports the pandas API on Spark and converts the Spark DataFrame spark_df into a pandas-on-Spark DataFrame, allowing the data scientist to use familiar pandas functions for further feature engineering.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark


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

Antwort: C

Begründung:
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


56. Frage
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. Precision
  • B. RMSE
  • C. Area under the residual operating curve
  • D. Accuracy
  • E. Recall

Antwort: E

Begründung:
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).


57. Frage
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

  • A. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
  • B. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
  • C. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
  • D. One-hot encoding is dependent on the target variable's values which differ for each application.
  • E. One-hot encoding is not supported by most machine learning libraries.

Antwort: A

Begründung:
One-hot encoding transforms categorical variables into a format that can be provided to machine learning algorithms to better predict the output. However, when done prematurely or universally within a feature repository, it can be problematic:
Dimensionality Increase: One-hot encoding significantly increases the feature space, especially with high cardinality features, which can lead to high memory consumption and slower computation.
Model Specificity: Some models handle categorical variables natively (like decision trees and boosting algorithms), and premature one-hot encoding can lead to inefficiency and loss of information (e.g., ordinal relationships).
Sparse Matrix Issue: It often results in a sparse matrix where most values are zero, which can be inefficient in both storage and computation for some algorithms.
Generalization vs. Specificity: Encoding should ideally be tailored to specific models and use cases rather than applied generally in a feature repository.
Reference
"Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson (CRC Press, 2019).


58. Frage
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