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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

• 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

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

 

Databricks Certified Machine Learning Associate Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
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. Spark ML
• B. Scikit-learn
• C. PvTorch
• D. pandas
• E. Keras

Answer: A

Explanation:
Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.
Reference:
Spark MLlib documentation (Feature Engineering with Spark ML).

 

NEW QUESTION # 31
A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

• A. The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.
• B. The F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.
• C. The F1 score should be utilized over accuracy when there are greater than two classes in the target variable.
• D. The F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

Answer: D

Explanation:
The F1 score is the harmonic mean of precision and recall and is particularly useful in situations where there is a significant imbalance between positive and negative classes. When there is a class imbalance, accuracy can be misleading because a model can achieve high accuracy by simply predicting the majority class. The F1 score, however, provides a better measure of the test's accuracy in terms of both false positives and false negatives.
Specifically, the F1 score should be used over accuracy when:
There is a significant imbalance between positive and negative classes.
Avoiding false negatives is a priority, meaning recall (the ability to detect all positive instances) is crucial.
In this scenario, the F1 score balances both precision (the ability to avoid false positives) and recall, providing a more meaningful measure of a model's performance under these conditions.
Reference:
Databricks documentation on classification metrics: Classification Metrics

 

NEW QUESTION # 32
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(Iterator(spark_df))
• B. predict(spark_df.columns)
• C. mapInPandas(predict)
• D. predict(*spark_df.columns)
• E. mapInPandas(predict(spark_df.columns))

Answer: C

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 # 33
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 not supported by most machine learning libraries.
• 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 dependent on the target variable's values which differ for each application.
• D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
• E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

Answer: E

Explanation:
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).

 

NEW QUESTION # 34
A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

• A. They can start each child run inside the parent run's indented code block using mlflow.start runO
• B. They can specify nested=True when starting the parent run for the tuning process
• C. They can start each child run with the same experiment ID as the parent run
• D. They can specify nested=True when starting the child run for each unique combination of hyperparameter values
• E. They can turn on Databricks Autologging

Answer: D

Explanation:
To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specify nested=True when starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.
Reference:
MLflow Documentation (Managing Nested Runs).

 

NEW QUESTION # 35
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