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Professional-Data-Engineer Schulungsmaterialien & Professional-Data-Engineer Dumps Prüfung & Professional-Data-Engineer Studienguide

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q346-Q351):

346. Frage
How would you query specific partitions in a BigQuery table?

A. Use the EXTRACT(DAY) clause
B. Use DATE BETWEEN in the WHERE clause
C. Use the __PARTITIONTIME pseudo-column in the WHERE clause
D. Use the DAY column in the WHERE clause

Antwort: C

Begründung:
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02')

347. Frage
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

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

Antwort: C

Begründung:
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.

348. Frage
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

A. Create a Directed Acyclic Graph in Cloud Composer
B. Create a Cloud Dataproc Workflow Template
C. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster
D. Create an initialization action to execute the jobs

Antwort: B

Begründung:
Explanation/Reference: https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows

349. Frage
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 2 answers.

A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
B. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
C. Introduce resource hierarchy to leverage access control policy inheritance.
D. Use Cloud Deployment Manager to automate access provision.
E. 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.

Antwort: B,D

Begründung:
Explanation/Reference:

350. Frage
You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for
3 years. You want to minimize storage costs. What should you do?

A. 1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Set a locked retention policy on the bucket.
3. Create a BigQuery external table on the exported files.
B. 1 Create a BigQuery table clone.
2. Query the clone when you need to perform analytics.
C. 1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Enable versionmg on the bucket.
3. Create a BigQuery external table on the exported files.
D. 1 Create a BigQuery table snapshot.
2 Restore the snapshot when you need to perform analytics.

Antwort: A

Begründung:
This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:
* Exporting table data | BigQuery | Google Cloud
* Storage classes | Cloud Storage | Google Cloud
* Retention policies and retention periods | Cloud Storage | Google Cloud
* Federated queries | BigQuery | Google Cloud

351. Frage
......

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