Updated Sep-2024 Exam Materials for You to Prepare & Pass Databricks-Machine-Learning-Professional Exam.
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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
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NEW QUESTION # 33
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. None of these should be completed as Step #3
- B. Measure the latency of the prediction time
- C. Obtain the observed values (actual) feature values
- D. Compute the evaluation metric using the observed and predicted values
- E. Retrain the model
Answer: A
NEW QUESTION # 34
Which of the following is a simple statistic to monitor for categorical feature drift?
- A. Mode, number of unique values, and percentage of missing values
- B. Percentage of missing values
- C. Mode
- D. Number of unique values
- E. None of these
Answer: A
NEW QUESTION # 35
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. It is not possible to automatically log MLflow runs.
- B. spark.conf.set("autologging", True)
- C. mlflow.autolog()
- D. mlflow.spark.autolog()
- E. mlflow.sklearn.autolog()
Answer: B
NEW QUESTION # 36
Which of the following Databricks-managed MLflow capabilities is a centralized model store?
- A. Model Registry
- B. Models
- C. Experiments
- D. Feature Store
- E. Model Serving
Answer: E
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_CREATED"
- B. "MODEL_VERSION_TRANSITIONED_STAGE"
- C. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
- D. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
- E. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
Answer: D
NEW QUESTION # 38
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. features_df.write.mode("fs").path("new_table")
- B.
- C.
- D. features_df.write.mode("feature").path("new_table")
- E.
Answer: E
NEW QUESTION # 39
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. Chi-squared test
- C. Kolmogorov-Smirnov (KS) test
- D. Summary statistics trends
- E. Jensen-Shannon test
Answer: D
NEW QUESTION # 40
Which of the following is a probable response to identifying drift in a machine learning application?
- A. None of these responses
- B. Retraining and deploying a model on more recent data
- C. Rebuilding the machine learning application with a new label variable
- D. All of these responses
- E. Sunsetting the machine learning application
Answer: A
NEW QUESTION # 41
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_model(model_uri, spark_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_batch(model_uri, df)
Answer: C
NEW QUESTION # 42
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. Logged data visualizations cannot be viewed in Databricks
- B. The Artifacts section of the MLflow Experiment page
- C. The Figures section of the MLflow Run page
- D. The Artifacts section of the MLflow Run page
- E. The MLflow Model Registry Model paqe
Answer: C
NEW QUESTION # 43
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, "model")
- B. mlflow.register_model(run_id, "best_model")
- C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
- D. mlflow.register_model(model_uri, "best_model")
- E. mlflow.register_model(f"runs:/{run_id}/model")
Answer: A
NEW QUESTION # 44
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/recommender/stage-production/invocations
- B. https://<databricks-instance>/model/recommender/Production/invocations
- C. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
- D. https://<databricks-instance>/model-serving/recommender/Production/invocations
- E. The version number of the model version in Production is necessary to complete this task.
Answer: E
NEW QUESTION # 45
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.spark.track_model(sklearn_model, "model")
- B. mlflow.spark.log_model(sklearn_model, "model")
- C. mlflow.sklearn.log_model(sklearn_model, "model")
- D. mlflow.sklearn.load_model("model")
- E. mlflow.sklearn.track_model(sklearn_model, "model")
Answer: A
NEW QUESTION # 46
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: C
NEW QUESTION # 47
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 # 48
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 fit
- B. The same preprocessing logic will automatically be applied when calling predict
- C. There is no longer a need for pipeline-like machine learning objects
- D. The pvfunc model can be used to deploy models in a parallelizable fashion
- E. This approach has no impact when loading the logged Pvfunc model for downstream deployment
Answer: C
NEW QUESTION # 49
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?
- A. mlflow.models.schema.infer_schema
- B. There is no way to obtain the input schema and the output schema of an unlogged model.
- C. mlflow.models.Model.get_input_schema
- D. mlflow.models.signature.infer_signature
- E. mlflow.models.Model.signature
Answer: A
NEW QUESTION # 50
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. Batch serving has built-in capabilities in Databricks Machine Learning
- B. Testing is not possible in real-time serving deployments
- C. Querying stored predictions can be faster than computing predictions in real-time
- D. There is no advantage to using batch serving deployments over real-time serving deployments
- E. Computing predictions in real-time provides more up-to-date results
Answer: A
NEW QUESTION # 51
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. Streaming
- E. On-device
Answer: D
NEW QUESTION # 52
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. Label drift
- C. Prediction drift
- D. None of these
- E. Feature drift
Answer: E
NEW QUESTION # 53
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Updated Databricks-Machine-Learning-Professional Certification Exam Sample Questions: https://www.validvce.com/Databricks-Machine-Learning-Professional-exam-collection.html
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