Google Updated Professional-Data-Engineer Exam Questions and Answers by elowen

To identify performance bottlenecks within a specific operation like a JOIN, you need to see the "Execution Details" or the Execution Graph. This tool provides a visual representation of the query plan, showing each stage of the query (Input, Compute, Output) and where the most time or resources were consumed.

Why C is correct: The Query History (or Job History) in the BigQuery console allows you to select a completed job and view its Execution Graph. This graph explicitly breaks down performance by stage, showing wait times, read times, and compute times. It is specifically designed to identify "bottlenecks" such as Data Skew or Shuffle issues during JOIN operations.

Correcting other options:

A (Audit Logs): Cloud Logging provides high-level metadata (who ran the query, when, and how much it cost), but it does not provide the granular execution plan details required to diagnose a JOIN bottleneck.

B (INFORMATION_SCHEMA): While this view provides great historical data for overall job performance (like total bytes or duration), it is tabular and less efficient than the visual Execution Graph for quickly spotting which specific stage of a complex JOIN is slow.

D (Dry Run): A dry run only estimates the number of bytes that will be read and checks syntax. It does not actually execute the query or generate a performance plan based on real data distribution, so it cannot identify execution bottlenecks.

In a RAG system, the "Retrieval" step relies on finding documents that are semantically similar to a user's query. This is achieved through Embeddings.

Semantic Meaning: Simple keyword search (Option D) fails when a user asks a question using different wording than the document. By converting text into high-dimensional numerical vectors (embeddings), the system can calculate mathematical "closeness" between a query and a document based on meaning rather than just shared characters.

Vector Search: These vectors are typically stored in a vector database (like Vertex AI Vector Search or AlloyDB with pgvector). This allows the RAG system to retrieve the most relevant "chunks" of text to feed into the Large Language Model (LLM).

Correcting other options:

B: Relational databases are not optimized for unstructured text retrieval based on semantic intent.

C: While DLP is a good security practice, it is a preprocessing step for safety, not a step that enables retrieval or preparation for a RAG system's core functionality.

D: Keyword indexing is the "old" way of searching and is significantly less effective than semantic vector search for modern AI applications.

To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here’s why option A is the best choice:

Cloud Functions:

Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.

This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.

Flexibility and Simplicity:

Using Cloud Functions allows you to leverage Python’s extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.

Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.

Performance:

Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.

They are designed to scale automatically based on demand, ensuring that your workflow remains performant.

Steps to Implement:

Write the Cloud Function:

Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.

Deploy the function to Google Cloud.

Invoke Cloud Function from Workflow:

Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.

steps:

- callCloudFunction:

call: http.post

args:

url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME

body:

key: value

Process Results:

Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.

Reference Links:

Google Cloud Functions Documentation

Using Workflows with Cloud Functions

Workflows Standard Library