A Databricks Job is the core service for scheduling and automating notebooks, JARs, or Python scripts. Combining it with MLflow autologging within the notebook provides a fully integrated, Databricks-native solution. This approach automates the weekly execution, and MLflow automatically captures parameters, metrics, artifacts, and model lineage with each run, enabling robust versioning and traceability. The other options are less efficient or lack key capabilities: DLT is not optimized for ML pipelines, external cron jobs break integration and add complexity, and manual execution is not scalable or reliable.

`SparkTrials` is the key integration between HyperOpt and Apache Spark. It is designed specifically for Databricks and similar environments. It parallelizes the execution of individual hyperparameter evaluation trials across the worker nodes of the Spark cluster. This solves the memory issue by distributing the load, leverages cluster resources efficiently, and significantly speeds up the search process compared to the single-machine `Trials` class which runs everything on the driver node. Simply increasing driver memory is a limited fix that doesn’t leverage scalability. Manual grid searches are inefficient and not scalable, and MLflow autologging tracks results but does not perform the optimization itself.

This is the standard, best-practice workflow for promoting and consuming a production model. Archiving the old production model (or moving it to ‘Archived’ stage) is good practice for lineage. Transitioning the new version to ‘Production’ updates the stage. Loading the model using the URI `’models:/prod_model/Production’` means the application always gets the current model in the Production stage without any code or configuration updates. This provides a seamless deployment mechanism. Using a specific version number (option 1) would require a code deployment for every model update. Aliases (option 2) are a valid alternative but the question asks for the standard stage-based approach. Option 3 is incorrect as comments don’t trigger changes and querying is inefficient.

This is a fundamental design decision in ML systems. Pre-computed features (e.g., stored in a feature store or table) can be retrieved with very low latency, which is crucial for real-time inference. However, they represent a snapshot in time and might not include the very latest user activity. On-demand computation uses the latest data, ensuring high feature freshness, but the computation time (e.g., scanning 7 days of data) can introduce unacceptable latency for real-time requests. The correct approach is to analyze the specific use case: how critical is millisecond latency vs. having the absolute latest data? The other options are absolute statements that ignore this essential trade-off.

Target leakage is a critical issue where information that is only available *after* the target event occurs is inadvertently used as a feature during training. This creates an unrealistically strong predictive signal during validation (like a 0.98 AUC) because the model is effectively ‘cheating’ by seeing data correlated with the answer. A classic example is using a ‘total future purchase amount’ to predict ‘will make a purchase’. Options 2 and 3 describe overfitting, which typically shows a large gap between training and validation scores, not an unrealistically high validation score. Option 4 is a metric calculation error, which is possible but less structurally insidious than target leakage.

Reproducibility requires controlling two main things: software environment and randomness. Setting the random seed for all relevant libraries (NumPy, PyTorch/TensorFlow, Scikit-Learn) controls the stochastic elements of the algorithms and data shuffling. Using the same Databricks runtime version (which includes specific versions of Python, Spark, and ML libraries) controls the software environment. This combination is the most direct and effective practice. Option 2 is overly restrictive. Option 3 is incomplete as it doesn’t address software versions or randomness. Option 4 is partially correct (MLflow logs help), but Databricks Jobs do not automatically guarantee identical environments unless the same runtime version is explicitly selected.

The first and most powerful optimization in Spark is to avoid Python UDFs (both regular and Pandas UDFs) whenever possible. Spark SQL’s built-in functions and column expressions are implemented in Scala/Java and are optimized by the Catalyst query optimizer and Tungsten execution engine. They operate on columnar data in the JVM, avoiding the serialization overhead and per-row processing cost of Python UDFs. If the logic can be expressed using native Spark functions, it will almost always be the fastest option. Pandas UDFs (option 1) are a good second choice if the logic is too complex for native functions. Options 2 and 3 do not address the core architectural inefficiency.

A core benefit of the Databricks Feature Store is its support for point-in-time correctness. To enable this, you must: 1) Have a timestamp key in your fact/label table (e.g., `observation_date`). 2) Have timestamp columns for each feature table indicating when the feature values were valid or updated. When you call `create_training_set` with the `timestamp_lookup_key` specified, the Feature Store performs an `AS OF` join. This ensures that for each record in the fact table, it joins only the feature values that were valid *at or before* that specific point in time, preventing data leakage from the future. The other options are manual workarounds that are error-prone and do not leverage the built-in, automated safety mechanism.

`mlflow.transformers` is a dedicated flavor for transformer models from libraries like Hugging Face. When you use `log_model`, it correctly serializes the entire model object, its tokenizer, and any necessary configuration files into the MLflow artifact store. During loading on a worker node, the model is deserialized from these local artifacts (which are automatically distributed via Spark), eliminating the need for each worker to download from the internet. This is the canonical, supported solution. Option 2 is incorrect because the local path on the driver’s machine won’t be accessible to workers. Option 3 is a workaround that doesn’t solve the root cause. Option 4 involves manual handling of DBFS paths, which is less robust and integrated than using the native transformer flavor.

A Databricks Job is the core service for scheduling and automating notebooks, JARs, or Python scripts. Combining it with MLflow autologging within the notebook provides a fully integrated, Databricks-native solution. This approach automates the weekly execution, and MLflow automatically captures parameters, metrics, artifacts, and model lineage with each run, enabling robust versioning and traceability. The other options are less efficient or lack key capabilities: DLT is not optimized for ML pipelines, external cron jobs break integration and add complexity, and manual execution is not scalable or reliable.0