Databricks-Machine-Learning-Professional受験記、Databricks-Machine-Learning-Professional基礎問題集

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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Available Languages:	English
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Real Exam Qty:	59
Related Certifications:	Databricks Certified Machine Learning Associate
Passing Score:	Not publicly disclosed (approximately 70%)
Exam Format:	Multi-select, Multiple choice
Exam Price:	$200 USD
Recommended Training:	Machine Learning at Scale Advanced Machine Learning Operations
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

>> Databricks-Machine-Learning-Professional受験記 <<

Databricks-Machine-Learning-Professional基礎問題集 & Databricks-Machine-Learning-Professional対応内容

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Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
トピック 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
トピック 3	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
トピック 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
トピック 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
トピック 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
トピック 7	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
トピック 8	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q93-Q98):

質問 # 93
A retail company wants to better forecast their sales of each SKU in every store in order to more accurately distribute their products. To achieve this, a Data Scientist proposes scaling their existing forecasting model to forecast individually for each combination of SKU and Store ID.
They have a cluster with 12 executors available in order to execute this. The current model is written using Pandas and the Prophet library for forecasting, with a Python function that receives a Pandas Data Frame with historic sales data as a parameter to train the forecasting model. For their next iteration, they want to improve the efficiency of this approach while using the least amount of effort and leveraging all available resources. Which approach will do this?

A. 1. Rewrite the function using the Generalized Linear Regression models available in Spark MLlib, which allows parameterizing to reflect a similar behavior to that of Prophet;
2. Perform a GROUP BY on the Spark DataFrame with the training data to group by Store and SKU;
3. Apply the Spark UDF to the grouped DataFrame with the apply function.
B. 1. Use Spark repartition(12) to scale across all executors;
2. Run to Pandas to convert the repartitioned DataFrame to Pandas so it can run on the original existing function;
3. Add the Store ID and SKU to the Prophet model as exogenous variables.
C. 1. Use a Spark query to identify all combinations of SKU and Store ID;
2. Run a loop through every combination calling the existing Python function using Spark repartition(12) function so it distributes across all available executors.
D. 1. Decorate the existing training function with the @pandas_udf;
2. On the Spark DataFrame with all training data perform a GROUP BY by Store and SKU;
3. Apply the decorated function to the grouped DataFrame with the apply function.

正解：D

解説：
A grouped Pandas UDF lets you keep the existing Prophet-based pandas training function while distributing execution across the cluster. Grouping the Spark DataFrame by SKU and Store ID and applying the function runs one model per group in parallel on the executors, improving efficiency and leveraging all available resources with minimal refactoring.

質問 # 94
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

A. The new model must perform better than the original model on the most recently available data
B. All of these statements must be true prior to deploying the new model
C. The now model must perform better than the original model on all of the available data
D. The new model must perform better than the original model on a random subset of the available data

正解：A

解説：
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

質問 # 95
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Querying stored predictions can be faster than computing predictions in real-time
B. Computing predictions in real-time provides more up-to-date results
C. There is no advantage to using batch serving deployments over real-time serving deployments
D. Testing is not possible in real-time serving deployments
E. Batch serving has built-in capabilities in Databricks Machine Learning

正解：E

質問 # 96
Feature drift occurs when there is a change in which element?

A. The distribution of a target variable
B. The relationship between input variables and target variables
C. The distribution of an input variable
D. The distribution of the predicted target given by the model

正解：C

解説：
Feature drift refers to a change in the distribution of one or more input features over time, even if the target variable and model relationship remain stable. This can degrade model performance because the model was trained on a different feature distribution than it encounters during inference.

質問 # 97
A machine learning engineer is monitoring label values for a production machine learning classification model. The engineer believes that the relative prevalence of the classes is becoming changing in more recent data. Which tool can the machine learning engineer use to assess their theory?

A. Jenson-Shannon distance
B. One-way Chi-squared Test
C. Kolmogorov-Smirnov (KS) test
D. Two-way Chi-squared Test

正解：B

解説：
A One-way Chi-squared Test is used to compare observed class distributions with expected distributions to determine if there has been a significant shift. This makes it suitable for detecting label drift in classification tasks, where the relative prevalence of classes may be changing over time.

質問 # 98
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Databricks-Machine-Learning-Professional基礎問題集: https://www.pass4test.jp/Databricks-Machine-Learning-Professional.html

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