Databricks Databricks-Machine-Learning-Associate Real Exams | Sample Databricks-Machine-Learning-Associate Questions

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Sample Databricks-Machine-Learning-Associate Questions & Databricks-Machine-Learning-Associate Study Test

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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.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

A. The cross-validation process will no longer be reproducible
B. The feature engineering stages will be computed using validation data
C. The model will take longer to train for each unique combination of hvperparameter values
D. The cross-validation process will no longer be
E. The model will be refit one more per cross-validation fold

Answer: B

Explanation:
If the model object is passed to the estimator parameter of CrossValidator and the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.
Reference:
Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

NEW QUESTION # 57
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Iterative optimization
B. Spark ML cannot distribute linear regression training
C. Least-squares method
D. Logistic regression
E. Singular value decomposition

Answer: A

Explanation:
For large datasets with many variables, Spark ML distributes the training of a linear regression model using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient Descent or L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) to iteratively minimize the loss function. These iterative methods are suitable for distributed computing environments and can handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing parallel updates.
Reference:
Spark MLlib Documentation (Linear Regression with Iterative Optimization).

NEW QUESTION # 58
A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

A. They can set the runtime-version variable in their Spark session to "ml".
B. They can check the Databricks Runtime ML box when creating their clusters.
C. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
D. They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.

Answer: C

Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

NEW QUESTION # 59
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 if-else logic determining which model to use to compute each prediction
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 latency that is larger than the size of the original model
D. When the new solution requires that each model computes a prediction for every record
E. When the new solution's models have an average size that is larger than the size of the original model

Answer: D

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 # 60
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 child run for each unique combination of hyperparameter values
C. They can start each child run with the same experiment ID as the parent run
D. They can turn on Databricks Autologging
E. They can specify nested=True when starting the parent run for the tuning process

Answer: B

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 # 61
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DATABRICKS DATABRICKS-MACHINE-LEARNING-ASSOCIATE REAL EXAMS | SAMPLE DATABRICKS-MACHINE-LEARNING-ASSOCIATE QUESTIONS

Databricks Databricks-Machine-Learning-Associate Real Exams | Sample Databricks-Machine-Learning-Associate Questions