2024 Updates For the Latest Databricks-Machine-Learning-Professional Free Exam Study Guide!
Best Databricks-Machine-Learning-Professional Exam Preparation Material with New Dumps Questions
NEW QUESTION # 20
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.sklearn.track_model(sklearn_model, "model")
- C. mlflow.spark.log_model(sklearn_model, "model")
- D. mlflow.sklearn.log_model(sklearn_model, "model")
- E. mlflow.spark.track_model(sklearn_model, "model")
Answer: E
NEW QUESTION # 21
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.write_table
- B. There is no way to accomplish this task with fs
- C. fs.get_table
- D. fs.read_table
- E. fs.create_table
Answer: E
NEW QUESTION # 22
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. Computing predictions in real-time provides more up-to-date results
- C. Batch serving has built-in capabilities in Databricks Machine Learning
- D. Querying stored predictions can be faster than computing predictions in real-time
- E. There is no advantage to using batch serving deployments over real-time serving deployments
Answer: C
NEW QUESTION # 23
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: D
NEW QUESTION # 24
Which of the following describes concept drift?
- A. Concept drift is when there is a change in the relationship between input variables and target variables
- B. None of these describe Concept drift
- C. Concept drift is when there is a change in the distribution of a target variable
- D. Concept drift is when there is a change in the distribution of the predicted target given by the model
- E. Concept drift is when there is a change in the distribution of an input variable
Answer: D
NEW QUESTION # 25
Which of the following statements describes streaming with Spark as a model deployment strategy?
- A. The inference of incrementally processed records as soon as trigger is hit
- B. The inference of all types of records in real-time
- C. The inference of batch processed records as soon as a Spark job is run
- D. The inference of incrementally processed records as soon as a Spark job is run
- E. The inference of batch processed records as soon as a trigger is hit
Answer: D
NEW QUESTION # 26
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Models
- B. Model Registry
- C. Experiments
- D. Model Serving
- E. Feature Store
Answer: D
NEW QUESTION # 27
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. Two-way Chi-squared Test
- D. None of these
- E. One-way Chi-squared Test
Answer: E
NEW QUESTION # 28
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 Figures section of the MLflow Run page
- C. The MLflow Model Registry Model paqe
- D. Logged data visualizations cannot be viewed in Databricks
- E. The Artifacts section of the MLflow Experiment page
Answer: B
NEW QUESTION # 29
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 E
- B. Option C
- C. Option A
- D. Option B
- E. Option D
Answer: C
NEW QUESTION # 30
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(run_id, "best_model")
- C. mlflow.register_model(model_uri, "model")
- D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
- E. mlflow.register_model(model_uri, "best_model")
Answer: C
NEW QUESTION # 31
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable. They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df.
Which of the following code blocks can they use to create a function called predict that they can use to complete the task?
- A.
- B.
- C.
- D.
- E. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
Answer: A
NEW QUESTION # 32
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. mlflow.log_artifact
- C. mlflow.log_metric
- D. mlflow.log_model
- E. There is no way to store values like this.
Answer: C
NEW QUESTION # 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 predict with a stream-friendly prediction function
- B. Replace formatfdelta") with format("stream")
- C. Replace "inference" with the path to the location of the Delta table
- D. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
- E. Replace spark.read with spark.readStream
Answer: D
NEW QUESTION # 34
Which of the following machine learning model deployment paradigms is the most common for machine learning projects?
- A. Real-time
- B. None of these deployments
- C. Batch
- D. On-device
- E. Streaming
Answer: E
NEW QUESTION # 35
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. Retrain the model
- B. Compute the evaluation metric using the observed and predicted values
- C. Obtain the observed values (actual) feature values
- D. Measure the latency of the prediction time
- E. None of these should be completed as Step #3
Answer: E
NEW QUESTION # 36
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
- A. client.delete_model
- B. client.delete_model_version
- C. client.transition_model_version_stage
- D. client.delete_registered_model
- E. client.update_registered_model
Answer: D
NEW QUESTION # 37
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:
Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?
- A. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- B. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- C. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
- D. "MODEL_VERSION_CREATED"
- E. "MODEL_VERSION_TRANSITIONED_STAGE"
Answer: C
NEW QUESTION # 38
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?
- A. Edge/on-device
- B. Real-time
- C. Batch
- D. Streaming
- E. None of these strategies will accomplish the task.
Answer: D
NEW QUESTION # 39
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.
- E. features_df.write.mode("feature").path("new_table")
Answer: D
NEW QUESTION # 40
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. mlflow.search_runs(exp_id)
- C. spark.read.format("delta").load(exp_id)
- D. spark.read.format("mlflow-experiment").load(exp_id)
- E. There is no way to programmatically return row-level results from an MLflow Experiment.
Answer: C
NEW QUESTION # 41
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. AutoML
- B. MLflow
D Delta Lake - C. Spark UDFs
- D. [Structured Streaming
Answer: C
NEW QUESTION # 42
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model.
Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?
- A. None. Staging. Production. Archived
- B. Production
- C. [None. Staging. Production
- D. Staging. Production
- E. Staging. Production. Archived
Answer: D
NEW QUESTION # 43
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. There no changes necessary
- D. Replace client.update_registered_model with mlflow
- E. Add a Python model as an argument to update_registered_model
Answer: C
NEW QUESTION # 44
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