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Excellent Professional-Data-Engineer Exam Cram Questions–Find Shortcut to Pass Professional-Data-Engineer Exam

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Google Certified Professional Data Engineer exam is a comprehensive test that evaluates the candidate's proficiency in a variety of areas related to data processing systems. Professional-Data-Engineer Exam covers topics such as the design and implementation of data processing systems, the management of data storage and processing systems, and the optimization of data processing workflows. Professional-Data-Engineer exam is designed to be challenging and requires a deep understanding of data processing systems and the Google Cloud Platform.

Google Professional-Data-Engineer certification is a highly respected certification that is designed to help data professionals demonstrate their expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is recognized by many organizations as a mark of excellence in data engineering and is designed to help professionals advance their careers and gain recognition for their expertise in the field. Google Certified Professional Data Engineer Exam certification is challenging and requires a deep understanding of data processing systems and the Google Cloud Platform, making it an excellent credential for individuals who are looking to enhance their skills and knowledge in this area.

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 Certified Professional Data Engineer Exam Sample Questions (Q166-Q171):

NEW QUESTION # 166
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A. 1 TB
B. 1 GB
C. 500 TB
D. 500 GB

Answer: A

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options

NEW QUESTION # 167
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Option A
B. Option C
C. Option B.
D. Option D

Answer: A

NEW QUESTION # 168
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your dat
a. What should you do?

A. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.
B. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
C. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
D. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

Answer: A

NEW QUESTION # 169
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

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

Answer: D

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

NEW QUESTION # 170
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
C. Use Cloud Deployment Manager to automate access provision.
D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

Answer: B,C

NEW QUESTION # 171
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