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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q277-Q282): 277. Frage Which is not a valid reason for poor Cloud Bigtable performance?

A. There are issues with the network connection. B. The table's schema is not designed correctly. C. The Cloud Bigtable cluster has too many nodes. D. The workload isn't appropriate for Cloud Bigtable. Antwort: C

Begründung: The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded. Reference: https://cloud.google.com/bigtable/docs/performance

1. Frage Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A. Salting B. Hashing C. Randomization D. Field promotion Antwort: D

Begründung: Explanation By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries. Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

1. Frage Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of dat a. They want to improve this performance while minimizing cost. What should they do?

A. The performance issue should be resolved over time as the site of the BigDate cluster is increased. B. Redefine the schema by evenly distributing reads and writes across the row space of the table. C. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster. D. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers. Antwort: B

1. Frage You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join. B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports. C. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges. D. Add capacity (memory and disk space) to the database server by the order of 200. Antwort: C

1. Frage 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. 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. C. 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. D. 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. Antwort: D

Begründung: The key requirements are: Data on Cloud Storage (migrated from HDFS). Processing with Spark and SQL. Column-level security. Cost-effective and scalable for a data mesh. Let's analyze the options: Option A (Load to BigQuery tables, policy tags, Spark-BQ connector/BQ SQL): Pros: BigQuery native tables offer excellent performance. Policy tags provide robust column-level security managed centrally in Data Catalog. The Spark-BigQuery connector allows Spark to read from/write to BigQuery. BigQuery SQL is powerful. Scales well. Cons: “Loading” the data into BigQuery means moving it from Cloud Storage into BigQuery's managed storage. This incurs storage costs in BigQuery and an ETL step. While effective, it might not be the most “cost-effective” if the goal is to query data in place on Cloud Storage, especially for very large datasets. Option B (Long-living Dataproc, Hive, Ranger): Pros: Provides a Hadoop-like environment with Spark, Hive, and Ranger for column-level security. Cons: “Long-living Dataproc cluster” is generally not the most cost-effective, as you pay for the cluster even when idle. Managing Hive and Ranger adds operational overhead. While scalable, it requires more infrastructure management than serverless options. Option C (IAM at file level, BQ external table, Dataproc Spark): Pros: Using Cloud Storage is cost-effective for storage. BigQuery external tables allow SQL access. Cons: IAM at the file level in Cloud Storage does not provide column-level security. This option fails to meet a critical requirement. Option D (Define a BigLake table, policy tags, Spark-BQ connector/BQ SQL): Pros:BigLake Tables: These tables allow you to query data in open formats (like Parquet, ORC) on Cloud Storage as if it were a native BigQuery table, but without ingesting the data into BigQuery's managed storage. This is highly cost-effective for storage. Column-Level Security with Policy Tags: BigLake tables integrate with Data Catalog policy tags to enforce fine-grained column-level security on the data residing in Cloud Storage. This is a centralized and robust security model. Spark and SQL Access: Data scientists can use BigQuery SQL directly on BigLake tables. The Spark- BigQuery connector can also be used to access BigLake tables, enabling Spark processing. Cost-Effective & Scalable Data Mesh: This approach leverages the cost-effectiveness of Cloud Storage, the serverless querying power and security features of BigQuery/Data Catalog, and provides a clear path to building a data mesh by allowing different domains to manage their data in Cloud Storage while exposing it securely through BigLake. Cons: Performance for BigLake tables might be slightly different than BigQuery native storage for some workloads, but it's designed for high performance on open formats. Why D is superior for this scenario: BigLake tables (Option D) directly address the need to keep data in Cloud Storage (cost-effective for a data lake) while providing strong, centrally managed column-level security via policy tags and enabling both SQL (BigQuery) and Spark (via Spark-BigQuery connector) access. This is more aligned with modern data lakehouse and data mesh architectures than loading everything into native BigQuery storage (Option A) if the data is already in open formats on Cloud Storage, or managing a full Hadoop stack on Dataproc (Option B). Reference: Google Cloud Documentation: BigLake > Overview. “BigLake lets you unify your data warehouses and data lakes. BigLake tables provide fine-grained access control for tables based on data in Cloud Storage, while preserving access through other Google Cloud services like BigQuery, GoogleSQL, Spark, Trino, and TensorFlow.” Google Cloud Documentation: BigLake > Introduction to BigLake tables. “BigLake tables bring BigQuery features to your data in Cloud Storage. You can query external data with fine-grained security (including row- level and column-level security) without needing to move or duplicate data.” Google Cloud Documentation: Data Catalog > Overview of policy tags. “You can use policy tags to enforce column-level access control for BigQuery tables, including BigLake tables.” Google Cloud Blog: “Announcing BigLake – Unifying data lakes and warehouses” (and similar articles) highlight how BigLake enables querying data in place on Cloud Storage with BigQuery's governance features.

1. Frage ......

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