無料更新されたDatabricks Databricks-Machine-Learning-Professionalテストエンジン問題には62問あります [Q36-Q61]

無料更新されたDatabricks Databricks-Machine-Learning-Professionalテストエンジン問題には62問あります

ベストな問題集を使おうML Data Scientist Databricks-Machine-Learning-Professional専門試験問題

質問 # 36
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
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. mlflow.sklearn.load_model("model")
• B. mlflow.spark.track_model(sklearn_model, "model")
• C. mlflow.spark.log_model(sklearn_model, "model")
• D. mlflow.sklearn.track_model(sklearn_model, "model")
• E. mlflow.sklearn.log_model(sklearn_model, "model")

正解：B

質問 # 37
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. Obtain the observed values (actual) feature values
• B. None of these should be completed as Step #3
• C. Compute the evaluation metric using the observed and predicted values
• D. Retrain the model
• E. Measure the latency of the prediction time

正解：B

質問 # 38
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry?

• A. mlflow.register_model(f"runs:/{run_id}/model")
• B. mlflow.register_model(model_uri, "model")
• C. mlflow.register_model(model_uri, "best_model")
• D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
• E. mlflow.register_model(run_id, "best_model")

正解：B

質問 # 39
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

• A. The Artifacts section of the MLflow Run page
• B. The MLflow Model Registry Model paqe
• C. The Artifacts section of the MLflow Experiment page
• D. Logged data visualizations cannot be viewed in Databricks
• E. The Figures section of the MLflow Run page

正解：E

質問 # 40
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console.
Which of the following lines of code can they use to accomplish this task?

• A. mlflow.spark.autolog()
• B. mlflow.sklearn.autolog()
• C. spark.conf.set("autologging", True)
• D. It is not possible to automatically log MLflow runs.
• E. mlflow.autolog()

正解：C

質問 # 41
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

• A. Autoscaling clusters
• B. REST APIs
• C. Cloud-based compute
• D. None of these tools
• E. Containers

正解：C

質問 # 42
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

• A. None of these can be used to monitor feature drift
• B. Summary statistics trends
• C. Kolmogorov-Smirnov (KS) test
• D. Chi-squared test
• E. Jensen-Shannon test

正解：B

質問 # 43
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

• A. python_function can be used to store models in an MLmodel file
• B. python_function can be used to deploy models without worrying about which library was used to create the model
• C. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
• D. python_function can be used to deploy models in a parallelizable fashion
• E. python_function provides no benefits over the built-in library-specific model flavors

正解：D

質問 # 44
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

• A. Replace list with view in the endpoint URL
• B. Replace list with webhooks in the endpoint URL
• C. Replace POST with PUT in the call to http request
• D. Replace POST with GET in the call to http request
• E. There are no necessary changes

正解：B

質問 # 45
A machine learning engineer has deployed a model recommender using MLflow Model Serving. They now want to query the version of that model that is in the Production stage of the MLflow Model Registry.
Which of the following model URIs can be used to query the described model version?

• A. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
• B. The version number of the model version in Production is necessary to complete this task.
• C. https://<databricks-instance>/model/recommender/Production/invocations
• D. https://<databricks-instance>/model/recommender/stage-production/invocations
• E. https://<databricks-instance>/model-serving/recommender/Production/invocations

正解：B

質問 # 46
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

• A.
• B.
• C.
• D.
• E.

正解：C

質問 # 47
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

• A. The same preprocessing logic will automatically be applied when calling predict
• B. There is no longer a need for pipeline-like machine learning objects
• C. The pvfunc model can be used to deploy models in a parallelizable fashion
• D. This approach has no impact when loading the logged Pvfunc model for downstream deployment
• E. The same preprocessing logic will automatically be applied when calling fit

正解：B

質問 # 48
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

• A. Replace update_registered_model with update_model_version
• B. Replace description with artifact
• C. Add a Python model as an argument to update_registered_model
• D. Replace client.update_registered_model with mlflow
• E. There no changes necessary

正解：E

質問 # 49
Which of the following describes label drift?

• A. Label drift is when there is a change in the distribution of an input variable
• B. Label drift is when there is a change in the relationship between input variables and target variables
• C. None of these describe label drift
• D. Label drift is when there is a change in the distribution of a target variable
• E. Label drift is when there is a change in the distribution of the predicted target given by the model

正解：A

質問 # 50
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?

• A.
• B.
• C.
• D.

正解：C

質問 # 51
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. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
• B. Ensuring that a built-in model flavor is used for the model logging
• C. Starting a manual child run within the objective function
• D. Startinq a manual parent run before calling fmin
• E. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt

正解：D

質問 # 52
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. DESCRIBE
• B. DESCRIBE HISTORY
• C. HISTORY
• D. TIMESTAMP
• E. VERSION

正解：B

質問 # 53
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. spark.read.format("mlflow-experiment").load(exp_id)
• B. There is no way to programmatically return row-level results from an MLflow Experiment.
• C. mlflow.search_runs(exp_id)
• D. spark.read.format("delta").load(exp_id)
• E. client.list_run_infos(exp_id)

正解：D

質問 # 54
In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?

• A. The launch of a new cost-efficient SQL endpoint
• B. CI/CD pipelines are not needed for machine learning pipelines
• C. The arrival of a new feature table in the Feature Store
• D. The launch of a new cost-efficient job cluster
• E. The arrival of a new model version in the MLflow Model Registry

正解：D

質問 # 55
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

• A. None of these reasons
• B. JS is more robust when working with large datasets
• C. JS is not normalized or smoothed
• D. JS does not require any manual threshold or cutoff determinations
• E. All of these reasons

正解：B

質問 # 56
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data.
Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

• A.
• B.
• C.
• D.
• E.

正解：C

質問 # 57
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

• A. Replace "inference" with the path to the location of the Delta table
• B. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
• C. Replace predict with a stream-friendly prediction function
• D. Replace spark.read with spark.readStream
• E. Replace formatfdelta") with format("stream")

正解：B

質問 # 58
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

• A. mlflow.load_model(model_uri)
• B. mlflow.sklearn.load_model(model_uri)
• C. This can only be viewed in the MLflow Experiments UI
• D. client.pyfunc.load_model(model_uri)
• E. client.list_artifacts(run_id)["feature-importances.csv"]

正解：A

質問 # 59
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. Jenson-Shannon distance
• B. Kolmogorov-Smirnov (KS) test
• C. Two-way Chi-squared Test
• D. None of these
• E. One-way Chi-squared Test

正解：E

質問 # 60
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