Databricks Certified Machine Learning Professional - Databricks-Machine-Learning-Professional Exam Practice Test

Question 1

A Machine Learning Engineer has a computer vision model in Databricks Model Serving that obscures sensitive data from images. Internal teams use the model throughout the work week when they request access to new files. Recently model users complained that the model takes much longer in the morning. This coincides with when people arrive at work and request files for the day. When the engineer reviews the endpoint health metrics, they see P50 model latency peaks around 9AM at 20 seconds. Request rate also peaks at 9AM at 15 requests/second. The GPU utilization is over 60%, GPU memory usage over 50%, and provisioned concurrency at 4 throughout the day. What can the engineer do to reduce user wait time when request rate peaks at 9AM each morning?

A. The endpoint is constrained by the GPU. Scale the endpoint vertically by changing the endpoint workload_type from "GPU_SMALL" to "GPU_MEDIUM".

B. The endpoint is constrained by its ability to handle simultaneous requests. Scale the endpoint horizontally by editing the endpoint workload_size from "Small" to "Medium" to increase the model concurrency.

C. The endpoint is constrained by its ability to handle simultaneous requests. Define a rate_1imit on the endpoint to spread out the requests over the course of the day.

D. The endpoint is constrained by its ability to handle simultaneous requests. Enable "scale_to_zero" on the endpoint to spin up additional endpoints quickly to handle the requests.

Correct Answer: B

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Question 2

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.format("delta").table(path).drop("star_rating")

B. Delta tables cannot be modified

C. spark.read.format("delta").load(path).drop("star_rating")

D. spark.read.table(path).drop("star_rating")

E. spark.sql("SELECT * EXCEPT star_rating FROM path")

Correct Answer: D

Question 3

Which MLflow function logs files like plots or datasets?

A. mlflow.log_metric()

B. mlflow.log_artifact()

C. mlflow.save_model()

D. mlflow.log_param()

Correct Answer: B

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Question 4

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. Batch

B. Real-time

C. Streaming

D. Edge/on-device

E. None of these strategies will meet the requirements.

Correct Answer: B

Question 5

A Data Scientist is building a product recommendation model that suggests additional items based on a customer's current shopping basket for an online merchant. The model needs to access the real-time basket contents at inference time to avoid recommending items already in the basket. The model will be deployed using Databricks Model Serving. The data scientist now wants to implement real-time feature engineering to incorporate the current basket contents into the model's prediction. Which approach will do this?

A. Write basket data to a feature table and publish to the online store for low-latency access.

B. Store basket data in a Delta table and configure automatic feature lookup during model serving.

C. Create a custom pyfunc model that processes the basket contents as part of the model's predict() method.

D. Implement feature preprocessing in a separate microservice that feeds the model serving endpoint.

Correct Answer: C

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Question 6

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 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_STAGING"

C. "MODEL_VERSION_TRANSITIONED_STAGE"

D. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"

E. "MODEL_VERSION_CREATED"

Correct Answer: B

Question 7

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 URls can be used to query the described model version?

A. https:///model/recommender/stage-production/invocations

B. https:///model/recommender/Production/invocations

C. https:///model-serving/recommender/Production/invocations

D. The version number of the model version in Production is necessary to complete this task.

Correct Answer: B

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Question 8

A Machine Learning Engineer is considering moving their functions and unit tests from notebooks into separate Python files (e.g., modules and test scripts) to take advantage of the numerous benefits of this approach like automated execution, code reusability, and version control. Which challenge should the engineer consider with this approach?

A. Separating code and tests often leads to decreased reliability and poor code quality.

B. It makes functions harder to import and reuse across different notebooks.

C. This change would prevent the use of any non-Python environments like Scala.

D. Managing a more complex project structure can be harder to maintain and navigate.

Correct Answer: D

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Question 9

Which of the following is a drawback associated with using Jensen-Shannon (JS) distance for numeric feature drift detection?

A. JS is not robust when working with large datasets

B. JS requires a manual threshold or cutoff determinations

C. All of these reasons

D. None of these reasons

Correct Answer: B

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Question 10

Which of the following describes concept drift?

A. Concept drift is when there is a change in the distribution of an input variable

B. Concept drift is when there is a change in the distribution of a target variable

C. Concept drift is when there is a change in the relationship between input variables and target variables

D. Concept drift is when there is a change in the distribution of the predicted target given by the model

E. None of these describe Concept drift

Correct Answer: D

Question 11

A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
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. log_artifact

B. log_param

C. There is no way to store values like this.

D. log_model

E. log_metric

Correct Answer: A

Question 12

A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company's cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider's Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
"cloud_creds" with key "db_key". These credentials can be used to authenticate to the cloud provider's SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

A. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Add "environment_vars":
{"DB_ACCESS_TOKEN": ""} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.

B. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Log the model to MLflow with the access token value as a custom MLflow artifact.
Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider's SDK.

C. Add "environment_vars": {"DB_ACCESS_TOKEN": "{{secrets/cloud_creds/db_key}}"} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.

D. Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
/.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.

Correct Answer: C

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Question 13

A Data Scientist is preparing a Spark ML pipeline on a customer dataset with numeric features age, annual_income, and transaction_count, each varying widely. Because the chosen algorithm requires inputs normalized to the [0,1] range, they need to apply the appropriate Spark ML transformer to these features. Which Spark ML transformer should the Data Scientist use to scale all features to the [0,1] range?

A. MinMaxScaler

B. RobustScaler

C. MaxAbsScaler

D. StandardScaler

Correct Answer: A

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Question 14

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. Which of the following code blocks can they use to accomplish the task?

A.

B.

C.

D.

E.

Correct Answer: A

Databricks Certified Machine Learning Professional - Databricks-Machine-Learning-Professional Exam Practice Test

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