[2024年03月27日] 手に入れよう！最新Databricks-Machine-Learning-Professional認定された有効な試験問題集解答
100%合格率保証付きの素晴らしいDatabricks-Machine-Learning-Professional試験問題PDF
Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲：
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質問 # 16
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. Startinq a manual parent run before calling fmin
- B. Starting a manual child run within the objective function
- C. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
- D. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
- E. Ensuring that a built-in model flavor is used for the model logging
正解：A
質問 # 17
Which of the following is a simple statistic to monitor for categorical feature drift?
- A. Mode
- B. None of these
- C. Number of unique values
- D. Percentage of missing values
- E. Mode, number of unique values, and percentage of missing values
正解：E
質問 # 18
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.sql("SELECT * EXCEPT star_rating FROM path")
- B. spark.read.table(path).drop("star_rating")
- C. spark.read.format("delta").table(path).drop("star_rating")
- D. Delta tables cannot be modified
- E. spark.read.format("delta").load(path).drop("star_rating")
正解：B
質問 # 19
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. Batch
- C. None of these strategies will meet the requirements.
- D. Real-time
- E. Streaming
正解：D
質問 # 20
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 # 21
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.add_model_version
- B. MlflowClient.get_model_version
- C. MlflowClient.update_registered_model
- D. The machine learning engineer needs to create an entirely new MLflow Model Registry model
- E. mlflow.register_model
正解：C
質問 # 22
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 provides no benefits over the built-in library-specific model flavors
- B. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
- C. python_function can be used to deploy models without worrying about which library was used to create the model
- D. python_function can be used to store models in an MLmodel file
- E. python_function can be used to deploy models in a parallelizable fashion
正解：E
質問 # 23
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?
- A. None of these tools
- B. Autoscaling clusters
- C. Containers
- D. REST APIs
- E. Cloud-based compute
正解：E
質問 # 24
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 pvfunc model can be used to deploy models in a parallelizable fashion
- B. The same preprocessing logic will automatically be applied when calling fit
- C. There is no longer a need for pipeline-like machine learning objects
- D. The same preprocessing logic will automatically be applied when calling predict
- E. This approach has no impact when loading the logged Pvfunc model for downstream deployment
正解：C
質問 # 25
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. None of these statements
- B. All of these statements
- C. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- D. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
- E. Because the streaming deployment is always on, all types of data must be handled without producing an error
正解：C
質問 # 26
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
- A. Streaming
- B. None of these deployments
- C. Batch
- D. Real-time
- E. On-device
正解：A
質問 # 27
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：B
質問 # 28
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:
The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?
- A. Indent the child run blocks within the parent run block
- B. Provide the same name to the run name parameter for all three run blocks
- C. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
- D. Remove the nested=True argument from the child runs
- E. Add the nested=True argument to the parent run
正解：C
質問 # 29
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_batch(model_uri, spark_df)
- B. fs.score_model(model_uri, spark_df)
- C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - D. fs.score_batch(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：A
質問 # 30
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?
- A. [Structured Streaming
- B. MLflow
D Delta Lake - C. AutoML
- D. Spark UDFs
正解：D
質問 # 31
Which of the following describes the concept of MLflow Model flavors?
- A. A convention that MLflow Experiments can use to organize their Runs by project
- B. A convention that MLflow Model Registry can use to organize its Models by project
- C. A convention that deployment tools can use to wrap preprocessing logic into a Model
- D. A convention that MLflow Model Registry can use to version models
- E. A convention that deployment tools can use to understand the model
正解：A
質問 # 32
Which of the following statements describes streaming with Spark as a model deployment strategy?
- A. The inference of batch processed records as soon as a Spark job is run
- B. The inference of incrementally processed records as soon as a Spark job is run
- C. The inference of all types of records in real-time
- D. The inference of incrementally processed records as soon as trigger is hit
- E. The inference of batch processed records as soon as a trigger is hit
正解：B
質問 # 33
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 formatfdelta") with format("stream")
- B. Replace spark.read with spark.readStream
- C. Replace "inference" with the path to the location of the Delta table
- D. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
- E. Replace predict with a stream-friendly prediction function
正解：D
質問 # 34
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