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Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

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Google Professional-Data-Engineer Certification Exam is highly respected in the industry and is a valuable asset for professionals who want to advance their careers in big data. Holding this certification demonstrates that a candidate has the skills and knowledge needed to design and build data processing systems on the Google Cloud Platform. It is also a testament to a candidate's dedication to advancing their skills and staying up-to-date with the latest technologies in the field.

Google Certified Professional Data Engineer Exam Sample Questions (Q294-Q299):

NEW QUESTION # 294
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 lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A. Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.
B. Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.
C. Use a Cloud SQL for PostgreSQL highly available instance first, and bagtable with US. Europe, and Asiareplication alter securing funding
D. Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.

Answer: A

Explanation:
https://cloud.google.com/spanner/docs/instance-configurations#tradeoffs_regional_versus_multi- region_configurations

NEW QUESTION # 295
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

A. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
B. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
C. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
D. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

Answer: A

Explanation:
By creating an authorized view one assures that the data is current and avoids taking more storage space (and cost) in order to share a dataset. B and D are not cost optimal and C does not guarantee that the data is kept updated.

NEW QUESTION # 296
You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

A. Create Dataplex data quality tasks.
B. Build Dataform assertions into your code
C. Build BigQuery user-defined functions (UDFs).
D. Write a Spark-based stored procedure.

Answer: B

Explanation:
Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query.
If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases.
Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions
|Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform

NEW QUESTION # 297
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?

A. single-keyed data with very high latency
B. single-keyed data with very low latency
C. multi-keyed data with very high latency
D. multi-keyed data with very low latency

Answer: B

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 298
You need to load a dataset with multiple terabytes of clickstream data into BigQuery. The data arrives each day as compressed JSON files in a Cloud Storage bucket. You need a low-cost, programmatic, and scalable solution to load the data into BigQuery. What should you do?

A. Use Cloud Data Fusion to create a pipeline to load the JSON files into BigQuery.
B. Create a Cloud Run function to run a Python script to read and parse each JSON file, and use the BigQuery streaming insert API.
C. Use the BigQuery Data Transfer Service from Cloud Storage.
D. Create an external table in BigQuery pointing to the Cloud Storage bucket and run the INSERT INTO ... SELECT * FROM external_table command.

Answer: D

Explanation:
Using an external table over the JSON files in Cloud Storage allows BigQuery to read the data directly without upfront ingestion costs. Running a load query from the external table into a native BigQuery table is fully programmatic, scales automatically for multi-terabyte datasets, supports compressed JSON, and is more cost-effective than streaming or managed ETL tools for batch ingestion.

NEW QUESTION # 299
......

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