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Google Professional-Data-Engineer certification exam is designed to validate the skills and knowledge of individuals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is intended for those professionals who have expertise in designing, building, and maintaining data processing systems using Google Cloud Platform services. Professional-Data-Engineer Exam evaluates the candidates' ability to design, implement, and manage data processing systems, as well as their understanding of data analysis and machine learning concepts.
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The Google Professional-Data-Engineer Exam consists of multiple-choice and multiple-select questions that assess the candidate's knowledge and skills in areas such as data ingestion, transformation, and storage on Google Cloud Platform. Professional-Data-Engineer exam also covers topics such as data processing using Apache Beam, data analysis using BigQuery, and machine learning using TensorFlow. Candidates who pass the exam will receive the Google Certified Professional Data Engineer certification, which is widely recognized in the industry as a mark of expertise in designing and implementing data solutions on Google Cloud Platform.
NEW QUESTION # 341
Your startup has a web application that currently serves customers out of a single region in Asia.
You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?
Answer: B
Explanation:
When you create a Cloud Spanner instance, you must configure it as either regional (that is, all the resources are contained within a single Google Cloud region) or multi-region (that is, the resources span more than one region).
You can change the instance configuration to multi-regional (or global) at anytime.
NEW QUESTION # 342
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
Answer: D
Explanation:
Reference: https://cloud.google.com/solutions/business-intelligence/
NEW QUESTION # 343
Which of the following statements is NOT true regarding Bigtable access roles?
Answer: D
Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.
NEW QUESTION # 344
Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
* The old data can be deleted anytime
* You plan to use the visualization layer for current and historical reporting
* The old data should be available instantly when accessed
* There should not be any charges for data retrieval.
What should you do to optimize for cost?
Answer: C
Explanation:
- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.
NEW QUESTION # 345
You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?
Answer: D
Explanation:
The requirements are:
Explore ORC formatted files with Hive partitioning.
Mimic the SQL on Hive query engine experience.
Cost-effective storage and processing.
Avoid impacting the on-premises Hadoop solution.
Let's analyze the options:
Option A (Import to Bigtable):Bigtable is a NoSQL database, not suited for SQL-based exploration of ORC files or Hive-style partitioning directly. This would require significant data transformation and a different query paradigm. Not cost-effective for this use case.
Option B (Import to BigQuery native tables):Importing data into BigQuery native storage is an option.
BigQuery can load ORC files. This provides excellent query performance. However, it involves an ETL step (importing) and storage costs for the datawithin BigQuery, which might be higher than keeping it in its original format on Cloud Storage if query patterns are exploratory and not extremely frequent on all data.
Option C (Copy to Cloud Storage, deploy Dataproc):Dataproc allows you to run Hadoop/Spark (and thus Hive) clusters on Google Cloud. This would provide a very similar experience ("SQL on the Hive query engine"). However, running a persistent Dataproc cluster incurs compute costs for the cluster nodes, even when not actively querying. While ephemeral clusters are possible, it adds operational overhead for exploratory queries. Storage on Cloud Storage is cost-effective.
Option D (Copy to Cloud Storage, create external BigQuery tables):This is often the most cost-effective and straightforward solution for this scenario.
Cost-effective Storage:Cloud Storage is a low-cost option for storing files like ORC.
SQL Interface:BigQuery provides a familiar SQL interface.
External Tables:BigQuery can query data directly from Cloud Storage (including ORC files) using external tables. This avoids the need to load data into BigQuery's managed storage, saving on storage costs and ETL effort.
Hive Partitioning:BigQuery external tables support Hive partitioning layouts. When you define the external table, you can specify the partitioning scheme, and BigQuery will use partition pruning to scan only relevant partitions, improving performance and reducing costs for queries that filter on partition keys. This directly mimics the Hive experience.
Processing Cost:You only pay for the data scanned by BigQuery queries, which aligns with exploratory analysis.
Comparing D with B: External tables are generally more cost-effective for storage and initial setup if the data is already in ORC and an ETL process into BigQuery native storage is to be avoided. Query performance might be slightly less than native tables but is often sufficient for exploration, especially with partitioning.
Comparing D with C: BigQuery external tables are serverless, meaning no cluster to manage or pay for when idle. Dataproc requires managing and paying for a cluster. For exploration, the serverless nature of BigQuery is usually more cost-effective.
Therefore, copying ORC files to Cloud Storage and using BigQuery external tables is the most cost-effective solution that meets all requirements.
Reference:
Google Cloud Documentation: BigQuery > External data sources > Querying Cloud Storage data. "You can query data in Cloud Storage by using external tables or federated queries. External tables are tables that read data directly from files in Cloud Storage." Google Cloud Documentation: BigQuery > External data sources > Supported formats and compression types. ORC is a supported format.
Google Cloud Documentation: BigQuery > Creating and using tables > Creating external tables. "External tables let you query data stored in Cloud Storage as if it were a standardBigQuery table. You can use external tables to query data in various formats, including... ORC..." Google Cloud Documentation: BigQuery > Creating and using tables > Querying partitioned external tables.
"You can create an external table that is partitioned on Hive partitioning keys. When you query a Hive partitioned external table, BigQuery performs partition pruning to skip reading unnecessary partitions." This directly addresses the "Hive partitioning" and "explore data in a similar way" requirements.
Google Cloud Blog: "Choosing the right data processing option on GCP: BigQuery vs. Dataproc" (and similar articles) often highlight BigQuery external tables as a cost-effective way to query data in place on Cloud Storage, especially for data lake scenarios.
NEW QUESTION # 346
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