Databricks-Machine-Learning-Professional인기자격증덤프자료 & Databricks-Machine-Learning-Professional퍼펙트덤프문제

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Databricks Databricks-Machine-Learning-Professional 시험요강:

주제	소개
주제 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
주제 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
주제 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
주제 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
주제 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
주제 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
주제 7	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
주제 8	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
주제 9	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
주제 10	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

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최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q157-Q162):

질문 # 157
A Machine Learning Engineer has a computer vision model in Databricks Model Serving that obscures sensitive data from images. Internal teams use the model throughout the work week when they request access to new files. Recently model users complained that the model takes much longer in the morning. This coincides with when people arrive at work and request files for the day. When the engineer reviews the endpoint health metrics, they see P50 model latency peaks around 9AM at 20 seconds. Request rate also peaks at 9AM at 15 requests/second. The GPU utilization is over 60%, GPU memory usage over 50%, and provisioned concurrency at 4 throughout the day. What can the engineer do to reduce user wait time when request rate peaks at 9AM each morning?

A. The endpoint is constrained by the GPU. Scale the endpoint vertically by changing the endpoint workload_type from "GPU_SMALL" to "GPU_MEDIUM".
B. The endpoint is constrained by its ability to handle simultaneous requests. Scale the endpoint horizontally by editing the endpoint workload_size from "Small" to "Medium" to increase the model concurrency.
C. The endpoint is constrained by its ability to handle simultaneous requests. Define a rate_1imit on the endpoint to spread out the requests over the course of the day.
D. The endpoint is constrained by its ability to handle simultaneous requests. Enable "scale_to_zero" on the endpoint to spin up additional endpoints quickly to handle the requests.

정답：B

설명：
The symptoms indicate a concurrency bottleneck during the 9AM traffic spike: request rate increases sharply, latency jumps, and the endpoint is already running at a fixed provisioned concurrency of 4. Increasing workload_size scales the endpoint horizontally to handle more concurrent requests, reducing queueing and bringing down user-perceived wait time during peak demand.

질문 # 158
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

A. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
B. Startinq a manual parent run before calling fmin
C. Ensuring that a built-in model flavor is used for the model logging
D. Starting a manual child run within the objective function
E. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt

정답：B

질문 # 159
Concept drift is when there is a change in which element?

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

정답：B

설명：
Concept drift occurs when the underlying relationship between input features and the target variable changes over time. This means that even if the feature and label distributions remain stable, the way features influence the target (the predictive relationship) has shifted, leading to reduced model accuracy unless retraining occurs.

질문 # 160
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

A. mlflow.register_model
B. MlflowClient.get_model_version
C. mlflow.add_model_version
D. The machine learning engineer needs to create an entirely new MLflow Model Registry model
E. MlflowClient.update_registered_model

정답：E

질문 # 161
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which operation can be used to load the model as an sklearn object for batch deployment?

A. mlflow.pyfunc.load_model(model_uri)
B. mlflow.sklearn.read_model(model_uri)
C. mlflow.sklearn.load_model(model_uri)
D. mlflow.spark.load_model(model_uri)
E. mlflow.pyfunc.read_model(model_uri)

정답：A

질문 # 162
......

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