Databricks-ML-Assoc Model Deployment Practice Test 2 — 15 Questions

Question 1

A machine learning engineer is preparing to deploy a custom MLflow model to a Databricks Model Serving endpoint. The model uses a third-party Python library that is not part of the Databricks Runtime ML base environment. The engineer wants to ensure the endpoint can load and run the model successfully. Which two actions are required? (Choose two.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Log the model with the required library captured in the model's conda or pip requirements.. Serving a custom model with a non-standard library requires two things: the library must be captured in the model's conda or pip requirements at log time so the serving container installs it, and the model must be registered in Unity Catalog so Model Serving can reference the version and its environment metadata. Cluster-level installs, init scripts, and runtime selection do not propagate into the managed serving environment.

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Install the third-party library on the driver node of the cluster used for training.

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Configure the endpoint to use a Databricks Runtime version that includes the third-party library.

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Add the third-party library to the cluster's init script so it loads at startup.

Question 2

A machine learning engineer has an MLflow model registered in Unity Catalog and wants to create a Databricks Model Serving endpoint for real-time inference. The endpoint must be able to scale down to zero when idle to minimize cost, while still handling bursts of traffic. Which configuration should the engineer choose?

Accepted Answer

Set `scale_to_zero_enabled` to true and configure a `workload_size` appropriate for the expected burst.. Databricks Model Serving endpoints can scale to zero when idle, eliminating compute cost between requests, and scale up when traffic arrives. Enabling `scale_to_zero_enabled` satisfies the cost goal, while selecting an appropriate `workload_size` ensures the endpoint has enough compute per instance to handle bursts. Together they deliver cost-efficient, responsive real-time inference.

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Enable autoscaling on the endpoint by setting `max_instances` to a high value and leave `scale_to_zero_enabled` at its default of false.

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Deploy the model to a single-node cluster and expose it via a REST API using a Databricks job schedule.

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Set `min_instances` to 0 and rely on the default `workload_size` to absorb traffic bursts without further configuration.

Question 3

A data scientist needs to deploy a model to Databricks Model Serving. Which component is strictly required to be logged in MLflow to enable the 'Model Serving' feature?

Accepted Answer

The model signature defining input and output schema. Databricks Model Serving requires a model artifact to be logged with a signature. The signature defines the expected input schema, which allows the serving endpoint to validate incoming requests. Without a signature, the model cannot be correctly parsed by the serving infrastructure, preventing the deployment from starting. This ensures that the production inference endpoint operates with expected data formats, maintaining reliability in downstream applications.

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The model's training accuracy metrics

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A Unity Catalog registered function

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A dedicated high-concurrency cluster

Question 4

When deploying a model using Model Serving, how does Databricks ensure that the environment remains consistent between the training workspace and the serving environment?

Accepted Answer

By capturing the model's dependencies during log_model. Databricks uses Conda or virtual environment dependencies captured during the MLflow logging process. When the model is logged, the environment details, including library versions, are saved. During deployment, the serving infrastructure creates a container that replicates this environment. This practice is crucial for avoiding 'dependency hell', where models fail in production due to subtle library version mismatches compared to the original training environment.

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By requiring the user to provide a Dockerfile

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By strictly enforcing the use of the latest stable libraries

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By running the model on the same training cluster

Question 5

Which strategy is most effective for managing model drift in a production Databricks environment?

Accepted Answer

Log inference data to Delta tables for analysis. Managing model drift requires active monitoring of input data and output predictions. Setting up Databricks Model Serving with feature tables allows for logging of inference requests and responses to a Delta table. By comparing these production distributions against the training data using tools like Great Expectations or custom SQL queries, teams can detect shifts in data patterns that necessitate retraining or model adjustments to maintain performance.

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Manually check the model accuracy once a year

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Re-deploy the model with new data every hour

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Use a static model that never changes

Question 6

Which THREE factors should be considered when choosing the 'workload size' (e.g., Small, Medium, Large) for a Databricks Model Serving endpoint?

Accepted Answer

The memory requirements of the model artifact. Selecting the correct workload size is a trade-off between latency requirements, compute costs, and the model's resource footprint. Larger models with high parameter counts often require 'Large' configurations to prevent memory exhaustion or high tail latency. Conversely, smaller models may perform adequately on 'Small' instances, which are cost-effective. Monitoring the endpoint's resource utilization metrics is key to right-sizing the deployment and ensuring optimal performance within the budget constraints.

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The total number of users in the workspace

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The color scheme of the MLflow UI

Question 7

When deploying a model to a production endpoint, what is the best practice for handling dependencies?

Accepted Answer

Include a requirements.txt file in the model artifact. The best practice is to ensure all dependencies are explicitly defined in the MLflow model artifact. Using custom environment files or letting MLflow infer them ensures that the production container is built using the exact versions of packages used in training. This minimizes the risk of production-level errors caused by version mismatches, ensuring stability and reproducibility across the model lifecycle in the Databricks ecosystem.

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Manually install dependencies in the cluster terminal

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Use the 'latest' tag for all library imports

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Only use libraries that come pre-installed in the Databricks Runtime

Question 8

Which Databricks feature allows you to manage the lifecycle of a model, including transitions from 'Staging' to 'Production'?

Accepted Answer

MLflow Model Registry. The MLflow Model Registry provides a centralized store for managing models throughout their lifecycle. It allows teams to track model versions, apply transitions (e.g., Staging to Production), and manage metadata. This central control is fundamental for MLOps, as it provides a clear record of which model is currently active in the production environment and ensures that only validated models are promoted to downstream systems.

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Databricks SQL

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Delta Live Tables

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Unity Catalog

Question 9

Refer to the exhibit. A user attempts to update a model stage in the Model Registry and receives this error. What is the most appropriate action to resolve this?

Accepted Answer

Request the workspace admin to update the model ACLs. The error clearly indicates a lack of permission to modify the model's state. In Databricks, access control lists (ACLs) are strictly enforced on objects in the registry. To perform administrative actions like changing a model's stage, the user must be assigned the appropriate permission level (CAN_MANAGE) by an owner or admin. This enforces the principle of least privilege, preventing unauthorized users from promoting unvetted models to production.

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Re-register the model under a new name

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Upgrade the model to a higher version

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Switch to a different Databricks cluster

Question 10

Which of the following is a primary benefit of using a model serving endpoint versus a batch inference job?

Accepted Answer

Real-time, low-latency inference. Model serving endpoints provide low-latency, real-time responses to individual requests via REST APIs. This is essential for applications like fraud detection or recommendation engines, where an immediate prediction is required for a user interaction. Batch inference jobs, conversely, are designed for high-throughput, asynchronous processing of large datasets at scheduled intervals. Understanding these use cases is vital for selecting the correct deployment architecture to meet performance and latency requirements.

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Lower cost for infrequent predictions

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Ability to process billions of rows at once

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Simplified tracking of data lineage

Question 11

What is the primary role of an 'MLflow Signature' during the model deployment phase?

Accepted Answer

To define the input and output schema for validation. The MLflow signature acts as a contract between the model and the application that consumes it. By defining expected input types and shapes, the signature enables automatic validation of incoming payloads before they reach the model. This prevents runtime errors, such as type mismatches or missing features, and ensures that the serving endpoint remains robust and reliable when receiving production traffic from various sources.

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To encrypt the model artifacts for security

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To determine the optimal number of cluster nodes

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To compress the model for faster deployments

Question 12

Why should you use an inference table in Databricks Model Serving?

Accepted Answer

To provide visibility into production inputs and predictions. Inference tables allow you to capture every request and response processed by a model endpoint. This is vital for monitoring model performance over time, debugging issues by examining specific input-output pairs, and detecting data drift. Without this captured data, you essentially have no visibility into how the model is performing in the real world, which makes it impossible to maintain and improve the system effectively.

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To increase the speed of model inference

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To automatically retrain the model on new data

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To secure the endpoint against malicious attacks

Question 13

What is the purpose of the 'Champion' model version in the Model Registry?

Accepted Answer

To serve as the model used by all users by default. The 'Champion' model is the designated version that currently handles the primary traffic for a deployment. By explicitly labeling a version as the Champion, teams can clearly identify the production-ready model. This provides a single source of truth for deployment automation, ensuring that when an endpoint is configured to serve the 'Champion', it is always running the intended, validated model without manual configuration changes.

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To identify the model that took the longest to train

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To represent a model that is currently being tested

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To store the source code for the model

Question 14

When deploying a model to Databricks Model Serving, what is the recommended way to handle sensitive credentials like database connection strings?

Accepted Answer

Use Databricks Secrets to reference credentials within the serving environment.. Using Databricks Secrets is the industry standard for managing sensitive information securely. By decoupling credentials from the model code or deployment configuration, you prevent hard-coding secrets which could be inadvertently exposed. This practice is vital for maintaining security compliance and protecting proprietary data access, as it enables centralized management, auditing, and rotation of keys without necessitating code changes in the model repository.

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Include credentials as environment variables in the model's conda.yaml file.

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Store the credentials in an encrypted text file inside the model package.

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Use a global variable within the model inference script.

Question 15

Refer to the exhibit. What is the most likely cause of this error in a deployed MLflow model?

Accepted Answer

The client application is sending data that does not conform to the defined input schema.. The error explicitly indicates a schema mismatch between the client's payload and the model's expected input signature. This frequently occurs when downstream applications send data without proper type casting. Validating the input data before sending it to the serving endpoint is a critical step in production MLOps to prevent type-related failures and ensure robust service interaction between disparate software components in a distributed architecture.

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The serving endpoint has reached its maximum memory capacity.

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The model has been corrupted during the transition to Production.

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The serving endpoint is not authenticated to access the data.