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Databricks-Machine-Learning-Professional試験復習 & Databricks-Machine-Learning-Professional模擬モード

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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 JIT feature values as a need for real-time deployment
• Describe how to list all webhooks and how to delete a webhook

トピック 2

• 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 3

• Identify less performant data storage as a solution for other use cases
• Describe why complex business logic must be handled in streaming deployments

トピック 4

• Describe the advantages of using the pyfunc MLflow flavor
• Manually log parameters, models, and evaluation metrics using MLflow

トピック 5

• Identify which code block will trigger a shown webhook
• Describe the basic purpose and user interactions with Model Registry

トピック 6

• Identify that data can arrive out-of-order with structured streaming
• Identify how model serving uses one all-purpose cluster for a model deployment

トピック 7

• 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

 

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q140-Q145):

質問 # 140
A Data Scientist at an online gaming company is creating a model to predict player churn. The company currently collects terabytes of player activity logs daily, which are stored in Databricks and processed for daily reporting. The Data Scientist has completed feature engineering and the resulting data is saved as a Delta Table with a size of 500GB. They need to next build the model for the most performant and cost-effective performance for Databricks. Which approach will do this?

• A. Load the feature data as a Spark DataFrame and train the model using SparkML's RandomForestClassifier on a multi-node Databricks cluster.
• B. Load the feature data as a Spark DataFrame and train the model using Spark's DeepspeedTorchDistributor on a multi-node Databricks cluster.
• C. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a single-node Databricks cluster.
• D. Load the feature data as a pandas DataFrame and train the model using scikit-learn's RandomForestClassifier on a multi-node Databricks cluster.

正解：A

解説：
A 500GB Delta Table is far beyond what is practical to load into a single pandas DataFrame, and scaling pandas-based scikit-learn training across nodes is not the right fit for this workload. Using a Spark DataFrame with Spark ML's RandomForestClassifier leverages distributed data processing and distributed model training on a multi-node cluster, which is the most performant and cost-effective approach for large tabular datasets in Databricks.

 

質問 # 141
A Machine Learning Engineer needs to develop a custom anomaly detection model that monitors the internal IT infrastructure of their company. The model takes in compute metrics, logs, and user data and generates a binary prediction. The engineer plans to deploy it as a Databricks Model Serving endpoint. In production there will only be one client calling the endpoint once every
15 seconds. Leadership sees the model as an important part of their operational improvement strategy so maintaining consistent, stable, low latency inference is a requirement while minimizing infrastructure costs. The engineer plans to deploy the endpoint via the MLflow Deployment SDK.
Which endpoint config for the MLflow Deployment SDK should the engineer select?

• A.
• B.
• C.
• D.

正解：D

解説：
With a single client making requests every 15 seconds, the traffic is steady and predictable, and low-latency inference is a strict requirement. Disabling scale-to-zero avoids cold start latency, ensuring consistent response times. Selecting a Small workload size minimizes infrastructure costs while still providing sufficient resources for a lightweight binary classification model, making this configuration the best balance between performance, stability, and cost.

 

質問 # 142
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

• A.
• B. mlflow.log_data(importance_path, "feature-importance.csv")
• C.
• D. mlflow.log_artifact(importance_path, "feature-importance.csv")
• E. None of these code blocks tan accomplish the task.

正解：C

 

質問 # 143
A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
In order to do this, they use the following incomplete piece of code:

Which option correctly states:
(i) The complete command to run model training;
(ii) The complete command to execute the prediction on test data;
(iii) The object type of the model object returned by the model
training command.

• A.
• B.
• C.
• D.

正解：B

解説：
In Spark MLlib, a Pipeline is trained using the fit method, which applies all estimator stages to the training DataFrame and returns a PipelineModel. Predictions are generated by calling transform on the fitted PipelineModel, which applies the full pipeline (tokenization, feature hashing, and logistic regression) to the test DataFrame.

 

質問 # 144
A Machine Learning Engineer is using joblibspark and MLflowCallback to perform a distributed hyperparameter tuning experiment via Optuna. Assuming they have a single objective they are optimizing for, what will be the default sampler implemented by Optuna?

• A. NSGAIISampler
• B. TPESampler
• C. RandomSampler
• D. GridSampler

正解：B

解説：
For single-objective optimization, Optuna uses the Tree-structured Parzen Estimator (TPE) sampler by default. TPESampler is a Bayesian optimization method that efficiently explores the hyperparameter space by modeling promising parameter regions, making it well suited for scalable hyperparameter tuning with joblibspark and MLflowCallback.

 

質問 # 145
......

あなたのキャリアでいくつかの輝かしい業績を行うことを望まないのですか。きっとそれを望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。では、IT業種で仕事しているあなたはどうやって自分のレベルを高めるべきですか。実は、Databricks-Machine-Learning-Professional認定試験を受験して認証資格を取るのは一つの良い方法です。Databricksの認定試験のDatabricks-Machine-Learning-Professional資格は非常に大切なものですから、Databricksの試験を受ける人もますます多くなっています。

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