PASS GUARANTEED GOOGLE - PROFESSIONAL-DATA-ENGINEER - HIGH HIT-RATE FREE GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM TEST QUESTIONS

Pass Guaranteed Google - Professional-Data-Engineer - High Hit-Rate Free Google Certified Professional Data Engineer Exam Test Questions

Pass Guaranteed Google - Professional-Data-Engineer - High Hit-Rate Free Google Certified Professional Data Engineer Exam Test Questions

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Google Certified Professional Data Engineer Exam Sample Questions (Q279-Q284):

NEW QUESTION # 279
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

  • A. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
  • B. Disable external IP addresses from worker VMs and enable Private Google Access.
  • C. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
  • D. Disable external IP addresses and establish a Private Service Connect endpoint IP address.

Answer: B

Explanation:
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. Reference:
Private Google Access overview
Using Private Google Access and Cloud NAT
Using custom containers with Dataflow


NEW QUESTION # 280
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

  • A. You expect future mutations to have different features from the mutated samples in the database.
  • B. There are roughly equal occurrences of both normal and mutated samples in the database.
  • C. You expect future mutations to have similar features to the mutated samples in the database.
  • D. There are very few occurrences of mutations relative to normal samples.
  • E. You already have labels for which samples are mutated and which are normal in the database.

Answer: C,D

Explanation:
Explanation
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection


NEW QUESTION # 281
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

  • A. Transform
  • B. PCollection
  • C. Pipeline
  • D. Sink API

Answer: A

Explanation:
Explanation
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model


NEW QUESTION # 282
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error. SELECT person FROM
`project1.example.table1` WHERE city = "London" How would you correct the error?

  • A. Change "person" to "person.city".
  • B. Add ", UNNEST(person)" before the WHERE clause.
  • C. Change "person" to "city.person".
  • D. Add ", UNNEST(city)" before the WHERE clause.

Answer: B

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy- sql#nested_repeated_results


NEW QUESTION # 283
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat A.
Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

  • A. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
  • B. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
  • C. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
  • D. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.

Answer: B


NEW QUESTION # 284
......

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