Free Practice Questions for Google Professional-Data-Engineer Exam

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.

Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive

If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.

What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.

For example, if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage—reads would be much more frequent in this case, and reads are much slower with HDD storage.

Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).

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.

Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Customcontainer images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. References:

Private Google Access overview

Using Private Google Access and Cloud NAT

Using custom containers with Dataflow