Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.

If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.

Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.

The columns can be grouped into two types—categorical and continuous columns:

A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.

A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.

Year of birth and income are continuous columns. Country is a categorical column.

You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.

Comprehensive and Detailed Explanation:

This question is about applying the two primary optimization techniques in BigQuery: partitioning and clustering.

Partitioning divides a table into smaller segments based on a date, timestamp, or integer column. When you filter a query on the partition column, BigQuery performs "partition pruning," meaning it only scans the data in the relevant partitions. Since queries frequently filter by "specific event date ranges," partitioning by the event date column is the ideal strategy to reduce the amount of data scanned, which lowers cost and improves performance.

Clustering sorts the data within each partition based on the values in one or more columns. When you filter on a clustered column, BigQuery can use the sorted order to avoid scanning all the data within the relevant partitions. Since queries also filter by user ID, clustering by the user ID column will further improve performance and can reduce costs for those queries.

Conclusion: Option A is the correct answer because it correctly applies both techniques according to best practices: partition on the range-filtered column (event date) and cluster on the point-lookup/high-cardinality filtered column (user ID).

Option B is incorrect because you cannot partition a BigQuery table by a string column like a UUID.

Option C is incorrect because ingestion-time partitioning is less precise. Partitioning directly on the event date column is more effective for queries that filter on the event date itself.

Option D is incorrect because while clustering is helpful, partitioning is the more critical optimization for date range queries and provides the biggest cost savings. The combination of both is optimal.

Reference (Google Cloud Documentation Concepts):

The Google Cloud documentation for "Introduction to partitioned tables" and "Introduction to clustered tables" provides clear guidance. The best practice is to partition by a date or timestamp column that is commonly used as a filter to prune data. It then recommends to cluster by columns that are frequently used in WHERE clauses for filtering or in JOIN clauses. The combination of partitioning and clustering is a powerful optimization strategy.