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NEW QUESTION # 31
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?
- A. Containers
- B. Autoscaling clusters
- C. REST APIs
- D. None of these tools
- E. Cloud-based compute
Answer: E
NEW QUESTION # 32
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. client.list_artifacts(run_id)["feature-importances.csv"]
- B. This can only be viewed in the MLflow Experiments UI
- C. client.pyfunc.load_model(model_uri)
- D. mlflow.sklearn.load_model(model_uri)
- E. mlflow.load_model(model_uri)
Answer: E
NEW QUESTION # 33
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.log_model(sklearn_model, "model")
- B. mlflow.spark.log_model(sklearn_model, "model")
- C. mlflow.spark.track_model(sklearn_model, "model")
- D. mlflow.sklearn.track_model(sklearn_model, "model")
- E. mlflow.sklearn.load_model("model")
Answer: C
NEW QUESTION # 34
Which of the following is a simple, low-cost method of monitoring numeric feature drift?
- A. Jensen-Shannon test
- B. Chi-squared test
- C. Summary statistics trends
- D. None of these can be used to monitor feature drift
- E. Kolmogorov-Smirnov (KS) test
Answer: C
NEW QUESTION # 35
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?
- A. Sending a message to a Slack channel when a model version transitions stages
- B. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
- C. Starting a testing job when a new model is registered
- D. None of these use cases require the use of an HTTP Webhook
- E. Sending an email alert when an automated testing Job fails
Answer: B
NEW QUESTION # 36
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run.
Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?
- A. mlflow.log_param
- B. There is no way to store values like this.
- C. mlflow.log_metric
- D. mlflow.log_artifact
- E. mlflow.log_model
Answer: C
NEW QUESTION # 37
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 POST with GET in the call to http request
- B. Replace list with view in the endpoint URL
- C. Replace POST with PUT in the call to http request
- D. Replace list with webhooks in the endpoint URL
- E. There are no necessary changes
Answer: D
NEW QUESTION # 38
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, df)
- B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df) - C. fs.score_model(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_batch(model_uri, spark_df)
Answer: E
NEW QUESTION # 39
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(run_id, "best_model")
- B. mlflow.register_model(f"runs:/{run_id}/model")
- C. mlflow.register_model(model_uri, "best_model")
- D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
- E. mlflow.register_model(model_uri, "model")
Answer: E
NEW QUESTION # 40
Which of the following is a simple statistic to monitor for categorical feature drift?
- A. None of these
- B. Percentage of missing values
- C. Number of unique values
- D. Mode, number of unique values, and percentage of missing values
- E. Mode
Answer: D
NEW QUESTION # 41
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?
- A. Z-Ordering
- B. Tuning the file size
- C. Write as a Parquet file
- D. Data skipping
- E. Bin-packing
Answer: B
NEW QUESTION # 42
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. There is no advantage to using batch serving deployments over real-time serving deployments
- B. Querying stored predictions can be faster than computing predictions in real-time
- C. Computing predictions in real-time provides more up-to-date results
- D. Batch serving has built-in capabilities in Databricks Machine Learning
- E. Testing is not possible in real-time serving deployments
Answer: D
NEW QUESTION # 43
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("mlflow-experiment").load(exp_id)
- C. mlflow.search_runs(exp_id)
- D. There is no way to programmatically return row-level results from an MLflow Experiment.
- E. spark.read.format("delta").load(exp_id)
Answer: E
NEW QUESTION # 44
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.
Answer: A
NEW QUESTION # 45
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 schema(schema) with option("maxFilesPerTriqqer", 1}
- B. Replace "inference" with the path to the location of the Delta table
- C. Replace spark.read with spark.readStream
- D. Replace formatfdelta") with format("stream")
- E. Replace predict with a stream-friendly prediction function
Answer: A
NEW QUESTION # 46
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. Add a Python model as an argument to update_registered_model
- C. Replace client.update_registered_model with mlflow
- D. There no changes necessary
- E. Replace update_registered_model with update_model_version
Answer: D
NEW QUESTION # 47
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. MlflowClient.update_registered_model
- B. mlflow.add_model_version
- C. The machine learning engineer needs to create an entirely new MLflow Model Registry model
- D. MlflowClient.get_model_version
- E. mlflow.register_model
Answer: A
NEW QUESTION # 48
Which of the following describes label drift?
- A. None of these describe label drift
- B. Label drift is when there is a change in the distribution of a target variable
- C. Label drift is when there is a change in the distribution of the predicted target given by the model
- 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 an input variable
Answer: E
NEW QUESTION # 49
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 specify which version of the registered MLflow Model should be used based on the given application's current scenario
- B. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
- C. The context parameter allows the user to document the performance of a model after it has been deployed
- D. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
- E. 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
Answer: A
NEW QUESTION # 50
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 Figures section of the MLflow Run page
- B. The MLflow Model Registry Model paqe
- C. Logged data visualizations cannot be viewed in Databricks
- D. The Artifacts section of the MLflow Run page
- E. The Artifacts section of the MLflow Experiment page
Answer: A
NEW QUESTION # 51
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").table(path).drop("star_rating")
- C. spark.read.format("delta").load(path).drop("star_rating")
- D. spark.sql("SELECT * EXCEPT star_rating FROM path")
- E. Delta tables cannot be modified
Answer: A
NEW QUESTION # 52
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 Client
- B. MLflow APIs
- C. Databricks REST APIs
- D. AutoML APIs
- E. Jobs cannot be created programmatically
Answer: C
NEW QUESTION # 53
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