Explanation

Comprehensive and Detailed Explanation:The error message "Resources exceeded" in BigQuery indicates that the query's execution plan is too complex or requires more computational resources (slots) than are available to it in the on-demand, fair-share pool.
Option D is the correct answer. BigQuery's on-demand pricing model uses a massive, shared pool of processing units called slots. While this pool is large, a single query cannot monopolize it, and there are limits to prevent runaway jobs. For consistently complex, high-resource queries, the solution is to switch to capacity- based pricing by purchasing slot reservations (e.g., using BigQuery editions). This provides your project with a dedicated, guaranteed amount of processing capacity, ensuring your complex queries have the resources they need to complete successfully.
Option A is incorrect because API request quotas relate to the number of API calls (e.g., how many jobs you can submit per minute), not the computational resources allocated to a single running query.
Option B is incorrect because table size limits are not related to query execution resources.
Option C is incorrect because while a syntax error would cause a query to fail, it would do so immediately with a syntax error message, not after 20 minutes with a "Resources exceeded" error. While optimizing the query is a good practice, the most direct way to solve a resource limit issue is to provide more resources.
Reference (Google Cloud Documentation Concepts):The Google Cloud documentation on "BigQuery pricing" explains the two main models: on-demand pricing and capacity-based pricing (editions). The
"Resources exceeded" error is a known limitation of the on-demand model for extremely demanding queries.
The documentation on "Introduction to slots" and "Reservations" explicitly presents purchasing dedicated slots as the solution for gaining more predictable and higher query performance for demanding workloads.

First we need to know who is accessing what then we can create suitable policies. Stackdriver is used to track access logs for Bigquery.

Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins
/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model

Explanation
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance