[2024年更新]Databricks-Machine-Learning-ProfessionalはML Data Scientistリアルな無料試験練習テスト
無料ML Data Scientist Databricks-Machine-Learning-Professional試験問題を提供します
Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲：
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質問 # 26
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline.
Which of the following Databricks tools can be used to programmatically create the Job?
- A. MLflow APIs
- B. Databricks REST APIs
- C. Jobs cannot be created programmatically
- D. MLflow Client
- E. AutoML APIs
正解：B
質問 # 27
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?
- A. Measure the latency of the prediction time
- B. None of these should be completed as Step #3
- C. Retrain the model
- D. Compute the evaluation metric using the observed and predicted values
- E. Obtain the observed values (actual) feature values
正解：B
質問 # 28
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- B. Sending an email alert when an automated testing Job fails
- C. None of these use cases require the use of an HTTP Webhook
- D. Starting a testing job when a new model is registered
- E. Sending a message to a Slack channel when a model version transitions stages
正解：A
質問 # 29
A data scientist wants to remove the star_rating column from the Delta table at the location path. To do this, they need to load in data and drop the star_rating column.
Which of the following code blocks accomplishes this task?
- A. spark.read.table(path).drop("star_rating")
- B. spark.read.format("delta").load(path).drop("star_rating")
- C. spark.sql("SELECT * EXCEPT star_rating FROM path")
- D. spark.read.format("delta").table(path).drop("star_rating")
- E. Delta tables cannot be modified
正解：A
質問 # 30
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A. client.update_registered_model
- B. client.delete_model_version
- C. client.transition_model_version_stage
- D. client.delete_registered_model
- E. client.delete_model
正解：D
質問 # 31
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?
- A. fs.create_table
- B. fs.write_table
- C. fs.get_table
- D. fs.read_table
- E. There is no way to accomplish this task with fs
正解：A
質問 # 32
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?
- A. log_param
- B. There is no way to store values like this.
- C. log_artifact
- D. log_model
- E. log_metric
正解：C
質問 # 33
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?
- A. fs.score_model(model_uri, spark_df)
- B. fs.score_batch(model_uri, spark_df)
- C. fs.score_batch(model_uri, df)
- D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - E. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)
正解：B
質問 # 34
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. The machine learning engineer needs to create an entirely new MLflow Model Registry model
- D. mlflow.add_model_version
- E. MlflowClient.update_registered_model
正解：E
質問 # 35
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
- A. Streaming
- B. On-device
- C. Real-time
- D. Batch
- E. None of these deployments
正解：A
質問 # 36
Which of the following lists all of the model stages are available in the MLflow Model Registry?
- A. Staging. Production. Archived
- B. Development. Staging. Production
- C. None. Staging. Production. Archived
- D. Development. Staging. Production. Archived
- E. None. Staging. Production
正解：B
質問 # 37
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?
- A. None of these
- B. Two-way Chi-squared Test
- C. Jenson-Shannon distance
- D. One-way Chi-squared Test
- E. Kolmogorov-Smirnov (KS) test
正解：D
質問 # 38
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?
- A. client.list_run_infos(exp_id)
- B. spark.read.format("delta").load(exp_id)
- C. There is no way to programmatically return row-level results from an MLflow Experiment.
- D. spark.read.format("mlflow-experiment").load(exp_id)
- E. mlflow.search_runs(exp_id)
正解：B
質問 # 39
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?
- A. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- B. Because the streaming deployment is always on, all types of data must be handled without producing an error
- C. None of these statements
- D. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
- E. All of these statements
正解：A
質問 # 40
Which of the following is a benefit of logging a model signature with an MLflow model?
- A. The schema of input data will be converted to match the signature
- B. The schema of input data can be validated when serving models
- C. The model will be secured by the user that developed it
- D. The model will have a unique identifier in the MLflow experiment
- E. The model can be deployed using real-time serving tools
正解：A
質問 # 41
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?
- A. Edge/on-device
- B. Real-time
- C. None of these strategies will meet the requirements.
- D. Streaming
- E. Batch
正解：B
質問 # 42
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?
- A. VERSION
- B. DESCRIBE HISTORY
- C. HISTORY
- D. TIMESTAMP
- E. DESCRIBE
正解：B
質問 # 43
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