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[General] Professional-Data-Engineer Guide Torrent - Latest Professional-Data-Engineer Tes

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【General】 Professional-Data-Engineer Guide Torrent - Latest Professional-Data-Engineer Tes

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Google Certified Professional Data Engineer Exam Sample Questions (Q102-Q107):NEW QUESTION # 102
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?
  • A. Either the state or the city columns in the [myproject:mydataset.mytable]table have too many NULL values
  • B. The [myproject:mydataset.mytable] table has too many partitions
  • C. Most rows in the [myproject:mydataset.mytable]table have the same value in the country column, causing data skew
  • D. Users are running too many concurrent queries in the system
Answer: D

NEW QUESTION # 103
You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?
  • A. Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.
  • B. Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.
  • C. Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery.
    Share the encryption key by following the principle of least privilege.
  • D. Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.
Answer: A
Explanation:
The core requirements are to protect sensitive data elements (data privacy) while retainingalldata for potential future use, and then using this preprocessed data for consumer analyses.
* Retaining All Data:This immediately makes option B (remove sensitive fields) unsuitable because it involves data loss.
* Protecting Sensitive Data for Analysis & Future Use:Masking is a de-identification technique that redacts or replaces sensitive data with a substitute, allowing the data structure and usability for analysis to be maintained without exposing the original sensitive values. This aligns with protecting data while still making it usable.
* Cloud Data Loss Prevention (DLP) API:This service is specifically designed to discover, classify, and protect sensitive data. It offers various de-identification techniques, including masking.
* Dataflow:This is a serverless, fast, and cost-effective service for unified stream and batch data processing. It's well-suited for transforming large datasets, such as those read from Cloud Storage, and can integrate with the DLP API for de-identification.
* Writing to BigQuery:BigQuery is an ideal destination for an organization-wide dataset for consumer analyses.
Therefore, using Dataflow to read the data from Cloud Storage, leveraging the Cloud DLP API tomask(a form of de-identification) the sensitive elements, and then writing the processed (masked) data to BigQuery is the most appropriate solution. This approach protects privacy for the consumer analyses dataset while the original, unaltered data can still be retained in the restricted Cloud Storage bucket for future use cases that might require access to the original sensitive information (under strict governance).
Let's analyze why other options are less suitable:
* Option B:"Remove sensitive fields" means data loss, which contradicts the requirement to retain all data for potential future use cases.
* Option C:Encrypting sensitive fields with Cloud KMS and writing them to BigQuery is a valid way to protect data. However, for "consumer analyses," masked data is generally more directly usable than encrypted data. Analysts would typically work with de-identified (e.g., masked) data rather than directly querying encrypted fields and managing decryption keys for analytical purposes. While decryption is possible, masking often provides a better balance of privacy and utility for broad analysis.
The question also implies creating a datasetforanalysis, where masking makes the data ready-to-use for that purpose. The original data remains in Cloud Storage.
* Option D:Using CMEK encrypts the entire object in Cloud Storage at rest. While this protects the data in Cloud Storage, federated queries from BigQuery would access the raw, unmasked data (assuming decryption occurs seamlessly). This doesn't address the preprocessing requirement of protectingcertain sensitive data elementswithin the data itself for theconsumer analysesdataset. The goal is to create a de- identified dataset for analysis, not just secure the raw data at rest.
Reference:
Google Cloud Documentation: Cloud Data Loss Prevention > De-identification overview. "De-identification is the process of removing identifying information from data. Cloud DLP uses de-identification techniques such as masking, tokenization, pseudonymization, date shifting, and more to help you protect sensitive data." Google Cloud Documentation: Cloud Data Loss Prevention > Basic de-identification > Masking. "Masking hides parts of data by replacing characters with a symbol, such as an asterisk (*) or hash (#)." Google Cloud Documentation: Dataflow > Overview. "Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing." Google Cloud Solution: Automating the de-identification of PII in large-scale datasets using Cloud DLP and Dataflow. This solution guide explicitly outlines using Dataflow and DLP API for de-identifying (including masking) data from Cloud Storage and loading it into BigQuery. "You can use Cloud DLP to scan data for sensitive elements andthen apply de-identification techniques such as redaction, masking, or tokenization." and "This tutorial uses Dataflow to orchestrate the de-identification process."

NEW QUESTION # 104
You have a variety of files in Cloud Storage that your data science team wants to use in their models Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?
  • A. Create an external table in BigQuery and use SQL to transform the data as necessary Provide the data science team access to the external tables to explore the raw data.
  • B. Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
  • C. Load the data into BigQuery and use SQL to transform the data as necessary Provide the data science team access to staging tables to explore the raw data.
  • D. Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
Answer: D
Explanation:
Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure.
The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep. References:
* Dataprep overview
* Dataprep features
* Dataprep and BigQuery integration

NEW QUESTION # 105
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled.
You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?
  • A. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
  • B. Update the job to increase the maximum number of workers.
  • C. Change the pipeline code, and introduce a Reshuffle step to prevent fusion.
  • D. Enable Vertical Autoscaling to let the pipeline use larger workers.
Answer: C
Explanation:
Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage.
This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. References:
* 1: Streaming pipelines
* 2: Batch vs Streaming Performance in Google Cloud Dataflow
* 3: Deploy Dataflow pipelines
* 4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
* 5: Managing costs for Dataflow batch and streaming data processing

NEW QUESTION # 106
You need to analyze user clickstream data to personalize content recommendations. The data arrives continuously and needs to be processed with low latency, including transformations such as sessionization (grouping clicks by user within a time window) and aggregation of user activity. You need to identify a scalable solution to handle millions of events each second and be resilient to late-arriving data. What should you do?
  • A. Use Firebase Realtime Database for ingestion and storage, and Cloud Run functions for processing and analytics.
  • B. Use Cloud Data Fusion for ingestion and transformation, and Cloud SQL for storage and analytics.
  • C. Use Pub/Sub for ingestion, Dataflow with Apache Beam for processing, and BigQuery for storage and analytics.
  • D. Use Cloud Storage for ingestion, Dataproc with Apache Spark for batch processing, and BigQuery for storage and analytics.
Answer: C
Explanation:
Comprehensive and Detailed Explanation:
This question requires a solution that excels at large-scale, stateful stream processing with sophisticated windowing and handling of out-of-order data.
Option C is the correct answer because this architecture is perfectly suited for the requirements.
Pub/Sub is the global, scalable ingestion service for continuous event data.
Dataflow, with the Apache Beam programming model, is specifically designed for complex stream processing. It has powerful, built-in support for different windowing strategies (including session windows for sessionization) and sophisticated triggers for handling late-arriving data. Its serverless nature ensures it scales to handle millions of events.
BigQuery is the ideal sink for the processed data, enabling large-scale analytics for the recommendation engine.
Option A is incorrect as Firebase and Cloud Run are more suited for application backends and are not designed for complex, stateful data processing pipelines at this scale.
Option B is incorrect because it describes a batch processing pattern. Using Cloud Storage for ingestion and Dataproc for batch processing would introduce high latency, failing the "low latency" requirement.
Option D is incorrect because Cloud Data Fusion is primarily a batch-oriented ETL/ELT tool, and Cloud SQL is not an analytical data warehouse capable of handling this scale of data for analytics.
Reference (Google Cloud Documentation Concepts):
This is another example of the canonical Pub/Sub -> Dataflow -> BigQuery streaming analytics pattern. The Apache Beam Programming Guide (which is the foundation for Dataflow) extensively covers concepts like Windowing (specifically SessionWindows) and Triggers for handling late data. These features are critical for accurately processing real-world event streams like clickstream data and are core strengths of Dataflow.

NEW QUESTION # 107
......
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