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Professional-Data-Engineer VCE Exam Download

Google Professional Data Engineer Exam Questions and Answers

Question 41

You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

Options:

A.

Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

B.

Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

C.

Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

D.

Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

Question 42

You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag template named "gdpr". The template contains one mandatory field, "has sensitive data~. with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the "has sensitive data" field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which "hass-ensitive-data" is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

Options:

A.

Create the "gdpr" tag template with private visibility. Assign the bigquery -dataViewer role to the HR group on the tables that contain sensitive data.

B.

Create the ~gdpr" tag template with private visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employeesgroup, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

C.

Create the "gdpr" tag template with public visibility. Assign the bigquery. dataViewer role to the HR group on the tables that containsensitive data.

D.

Create the "gdpr" tag template with public visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees.group, and assign the bijquery.dataViewer role to the HR group on the tables that contain sensitive data.

Question 43

You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

Options:

A.

Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.

B.

Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.

C.

Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.

D.

Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

Question 44

Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

Options:

A.

Enable data access logs in each Data Analyst’s project. Restrict access to Stackdriver Logging via Cloud IAM roles.

B.

Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts’ projects. Restrict access to the Cloud Storage bucket.

C.

Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.

D.

Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.