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NEW QUESTION # 15
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. Sending an email alert when an automated testing Job fails
- B. None of these use cases require the use of an HTTP Webhook
- C. Sending a message to a Slack channel when a model version transitions stages
- D. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- E. Starting a testing job when a new model is registered
Answer: D
NEW QUESTION # 16
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. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
- B. Remove the nested=True argument from the child runs
- C. Indent the child run blocks within the parent run block
- D. Provide the same name to the run name parameter for all three run blocks
- E. Add the nested=True argument to the parent run
Answer: A
NEW QUESTION # 17
A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which of the following types of drift is present in the above scenario?
- A. Concept drift
- B. Prediction drift
- C. Feature drift
- D. Label drift
- E. None of these
Answer: C
NEW QUESTION # 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. Delta tables cannot be modified
- B. spark.read.format("delta").load(path).drop("star_rating")
- C. spark.read.table(path).drop("star_rating")
- D. spark.read.format("delta").table(path).drop("star_rating")
- E. spark.sql("SELECT * EXCEPT star_rating FROM path")
Answer: C
NEW QUESTION # 19
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 description with artifact
- B. Replace update_registered_model with update_model_version
- C. Replace client.update_registered_model with mlflow
- D. There no changes necessary
- E. Add a Python model as an argument to update_registered_model
Answer: D
NEW QUESTION # 20
Which of the following describes the concept of MLflow Model flavors?
- A. A convention that deployment tools can use to understand the model
- B. A convention that MLflow Experiments can use to organize their Runs 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 MLflow Model Registry can use to organize its Models by project
Answer: B
NEW QUESTION # 21
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:
The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?
- A.
- B.
- C. features_df.write.mode("fs").path("new_table")
- D. features_df.write.mode("feature").path("new_table")
- E.
Answer: B
NEW QUESTION # 22
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. MLflow
D Delta Lake - B. [Structured Streaming
- C. AutoML
- D. Spark UDFs
Answer: D
NEW QUESTION # 23
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?
- A. Testing is not possible in real-time serving deployments
- B. There is no advantage to using batch serving deployments over real-time serving deployments
- C. Querying stored predictions can be faster than computing predictions in real-time
- D. Batch serving has built-in capabilities in Databricks Machine Learning
- E. Computing predictions in real-time provides more up-to-date results
Answer: D
NEW QUESTION # 24
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?
- A. The context parameter allows the user to document the performance of a model after it has been deployed
- B. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
- C. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
- D. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
- E. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
Answer: E
NEW QUESTION # 25
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?
- A. mlflow.shap
- B. mlflow.shap.log_explanation
- C. client.log_artifact
- D. mlflow.log_figure
- E. None of these operations can accomplish the task.
Answer: A
NEW QUESTION # 26
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
- A. Batch
- B. Real-time
- C. Streaming
- D. None of these strategies will accomplish the task.
- E. Edge/on-device
Answer: C
NEW QUESTION # 27
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(model_uri, "best_model")
- B. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
- C. mlflow.register_model(model_uri, "model")
- D. mlflow.register_model(run_id, "best_model")
- E. mlflow.register_model(f"runs:/{run_id}/model")
Answer: C
NEW QUESTION # 28
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 distribution of a target variable
- C. None of these describe label drift
- D. Label drift is when there is a change in the relationship between input variables and target variables
- E. Label drift is when there is a change in the distribution of the predicted target given by the model
Answer: A
NEW QUESTION # 29
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. The same preprocessing logic will automatically be applied when calling fit
- E. This approach has no impact when loading the logged Pvfunc model for downstream deployment
Answer: B
NEW QUESTION # 30
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. Kolmogorov-Smirnov (KS) test
- B. Jenson-Shannon distance
- C. None of these
- D. One-way Chi-squared Test
- E. Two-way Chi-squared Test
Answer: D
NEW QUESTION # 31
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 of the following code blocks will accomplish this task inside of an existing MLflow run block?
A)
B)
C) mlflow.log_data(importance_path, "feature-importance.csv")
D) mlflow.log_artifact(importance_path, "feature-importance.csv")
E) None of these code blocks tan accomplish the task.
- A. Option D
- B. Option B
- C. Option E
- D. Option C
- E. Option A
Answer: E
NEW QUESTION # 32
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. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - B. fs.score_batch(model_uri, df)
- C. fs.score_batch(model_uri, spark_df)
- D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df) - E. fs.score_model(model_uri, spark_df)
Answer: C
NEW QUESTION # 33
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?
- A. Cloud-based compute
- B. Containers
- C. None of these tools
- D. REST APIs
- E. Autoscaling clusters
Answer: A
NEW QUESTION # 34
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.track_model(sklearn_model, "model")
- B. mlflow.sklearn.log_model(sklearn_model, "model")
- C. mlflow.spark.log_model(sklearn_model, "model")
- D. mlflow.sklearn.load_model("model")
- E. mlflow.spark.track_model(sklearn_model, "model")
Answer: E
NEW QUESTION # 35
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 MLflow Model Registry Model paqe
- B. The Figures section of the MLflow Run page
- C. Logged data visualizations cannot be viewed in Databricks
- D. The Artifacts section of the MLflow Experiment page
- E. The Artifacts section of the MLflow Run page
Answer: B
NEW QUESTION # 36
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A. client.transition_model_version_stage
- B. client.delete_registered_model
- C. client.delete_model_version
- D. client.update_registered_model
- E. client.delete_model
Answer: B
NEW QUESTION # 37
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.
Answer: E
NEW QUESTION # 38
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. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
- C. Because the streaming deployment is always on, all types of data must be handled without producing an error
- D. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
- E. All of these statements
Answer: B
NEW QUESTION # 39
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