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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. Jenson-Shannon distance
• B. One-way Chi-squared Test
• C. None of these
• D. Two-way Chi-squared Test
• E. Kolmogorov-Smirnov (KS) test

A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

• A. Edge/on-device
• B. Batch
• C. Real-time
• D. Streaming

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 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.
• B. The endpoint is constrained by the GPU. Scale the endpoint vertically by changing the endpoint workload_type from "GPU_SMALL" to "GPU_MEDIUM".
• C. 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.
• D. 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.

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 predict with a stream-friendly prediction function
• C. Replace formatfdelta") with format("stream")
• D. Replace spark.read with spark.readStream
• E. Replace "inference" with the path to the location of the Delta table

A Data Scientist has created a sales forecasting model, named sales-forecasting. The model is deployed to a model serving endpoint with a schema. They need to invoke this model using Python and would prefer to use a SDK function to make the request. Which method suits these requirements?

• A. Use the built in ai_query function and provide the endpoints and request parameters.
• B. Import the MLflow Deployments class and use the ai_query method and provide the endpoints and request parameters.
• C. Make an API request to the MODEL_VERSION_URI and provide the dataframe_split as the request parameter.
• D. Import the MLflow Deployments class and use the predict method and provide the endpoint and input parameters.