ExamTopics Logo
Mail Us[email protected]
Menu
Google Discussions
exam questions

Exam Professional Data Engineer All Questions

View all questions & answers for the Professional Data Engineer exam
Go to Exam

Exam Professional Data Engineer topic 1 question 297 discussion

Actual exam question from Google's Professional Data Engineer
Question #: 297
Topic #: 1
[All Professional Data Engineer Questions]

You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

  • A. 1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled.
    2. Configure Ranger for column level security.
    3. Process with Dataproc Spark or Hive SQL.
  • B. 1. Define a BigLake table.
    2. Create a taxonomy of policy tags in Data Catalog.
    3. Add policy tags to columns.
    4. Process with the Spark-BigQuery connector or BigQuery SQL.
  • C. 1. Load the data to BigQuery tables.
    2. Create a taxonomy of policy tags in Data Catalog.
    3. Add policy tags to columns.
    4. Process with the Spark-BigQuery connector or BigQuery SQL.
  • D. 1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage.
    2. Define a BigQuery external table for SQL processing.
    3. Use Dataproc Spark to process the Cloud Storage files.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by scaenruy at Jan. 4, 2024, 12:15 p.m.

Comments

Chosen Answer:
This is a voting comment (?). It is better to Upvote an existing comment if you don't have anything to add.
Switch to a voting comment New
Submit Cancel
raaad
Highly Voted 9 months, 2 weeks ago
Selected Answer: B
- BigLake Integration: BigLake allows you to define tables on top of data in Cloud Storage, providing a bridge between data lake storage and BigQuery's powerful analytics capabilities. This approach is cost-effective and scalable. - Data Catalog for Governance: Creating a taxonomy of policy tags in Google Cloud's Data Catalog and applying these tags to specific columns in your BigLake tables enables fine-grained, column-level access control. - Processing with Spark and SQL: The Spark-BigQuery connector allows data scientists to process data using Apache Spark directly against BigQuery (and BigLake tables). This supports both Spark and SQL processing needs. - Scalability into a Data Mesh: BigLake and Data Catalog are designed to scale and support the data mesh architecture, which involves decentralized data ownership and governance.
upvoted 17 times
...
JyoGCP
Most Recent 8 months, 1 week ago
Selected Answer: B
Going with 'B' based on the comments
upvoted 1 times
...
Matt_108
9 months, 2 weeks ago
Selected Answer: B
Option B, agree with comments explanation
upvoted 1 times
...
Jordan18
9 months, 3 weeks ago
Selected Answer: B
BigLake leverages existing Cloud Storage infrastructure, eliminating the need for a dedicated Dataproc cluster, reducing costs significantly.
upvoted 4 times
...
scaenruy
9 months, 3 weeks ago
Selected Answer: C
C. 1. Load the data to BigQuery tables. 2. Create a taxonomy of policy tags in Data Catalog. 3. Add policy tags to columns. 4. Process with the Spark-BigQuery connector or BigQuery SQL.
upvoted 1 times
raaad
9 months, 2 weeks ago
- Option B offers a serverless approach that integrates Cloud Storage (as a data lake), BigLake (for table definition), Data Catalog (for data mesh), and BigQuery (for analytics), all of which are essential components of a flexible, scalable, and secure data platform.
upvoted 7 times
...
...
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.

Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.

SaveCancel
exam
Someone Bought Contributor Access for:
SY0-701
London, 1 minute ago