ExamGecko
Login
Home / Google / Professional Data Engineer / List of questions
Ask Question

Google Professional Data Engineer Practice Test - Questions Answers, Page 6

List of questions

Question 51

Report
Export
Collapse

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.

What is the most likely cause of this problem?

The CSV data loaded in BigQuery is not flagged as CSV.
The CSV data loaded in BigQuery is not flagged as CSV.
The CSV data has invalid rows that were skipped on import.
The CSV data has invalid rows that were skipped on import.
The CSV data loaded in BigQuery is not using BigQuery's default encoding.
The CSV data loaded in BigQuery is not using BigQuery's default encoding.
The CSV data has not gone through an ETL phase before loading into BigQuery.
The CSV data has not gone through an ETL phase before loading into BigQuery.
Suggested answer: B
asked 18/09/2024
Duane Innmon
24 questions

Question 52

Report
Export
Collapse

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Introduce data compression for each file to increase the rate file of file transfer.
Introduce data compression for each file to increase the rate file of file transfer.
Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
Suggested answer: C, E
asked 18/09/2024
Nogueira Elder
39 questions

Question 53

Report
Export
Collapse

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Redis
Redis
HBase
HBase
MySQL
MySQL
MongoDB
MongoDB
Cassandra
Cassandra
HDFS with Hive
HDFS with Hive
Suggested answer: B, D, F

Explanation:

Topic 5, Practice Questions

asked 18/09/2024
Edgar Alvarez
35 questions

Question 54

Report
Export
Collapse

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?

Add ", UNNEST(person)" before the WHERE clause.
Add ", UNNEST(person)" before the WHERE clause.
Change "person" to "person.city".
Change "person" to "person.city".
Change "person" to "city.person".
Change "person" to "city.person".
Add ", UNNEST(city)" before the WHERE clause.
Add ", UNNEST(city)" before the WHERE clause.
Suggested answer: A

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-legacysql#nested_repeated_results

asked 18/09/2024
Danyail Storey
28 questions

Question 55

Report
Export
Collapse

What are two of the benefits of using denormalized data structures in BigQuery?

Reduces the amount of data processed, reduces the amount of storage required
Reduces the amount of data processed, reduces the amount of storage required
Increases query speed, makes queries simpler
Increases query speed, makes queries simpler
Reduces the amount of storage required, increases query speed
Reduces the amount of storage required, increases query speed
Reduces the amount of data processed, increases query speed
Reduces the amount of data processed, increases query speed
Suggested answer: B

Explanation:

Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table.

Denormalization also makes queries simpler because you do not have to use JOIN clauses.

Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.

Reference:

https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

asked 18/09/2024
Gaetano Vito Fraccalvieri
37 questions

Question 56

Report
Export
Collapse

Which of these statements about exporting data from BigQuery is false?

To export more than 1 GB of data, you need to put a wildcard in the destination filename.
To export more than 1 GB of data, you need to put a wildcard in the destination filename.
The only supported export destination is Google Cloud Storage.
The only supported export destination is Google Cloud Storage.
Data can only be exported in JSON or Avro format.
Data can only be exported in JSON or Avro format.
The only compression option available is GZIP.
The only compression option available is GZIP.
Suggested answer: C

Explanation:

Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.

Reference: https://cloud.google.com/bigquery/docs/exporting-data

asked 18/09/2024
Leandro Franklin Franklin
43 questions

Question 57

Report
Export
Collapse

What are all of the BigQuery operations that Google charges for?

Storage, queries, and streaming inserts
Storage, queries, and streaming inserts
Storage, queries, and loading data from a file
Storage, queries, and loading data from a file
Storage, queries, and exporting data
Storage, queries, and exporting data
Queries and streaming inserts
Queries and streaming inserts
Suggested answer: A

Explanation:

Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.

Reference: https://cloud.google.com/bigquery/pricing

asked 18/09/2024
Andrey Scherbakov
39 questions

Question 58

Report
Export
Collapse

Which of the following is not possible using primitive roles?

Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
Give UserA owner access and UserB editor access for all datasets in a project.
Give UserA owner access and UserB editor access for all datasets in a project.
Give a user access to view all datasets in a project, but not run queries on them.
Give a user access to view all datasets in a project, but not run queries on them.
Give GroupA owner access and GroupB editor access for all datasets in a project.
Give GroupA owner access and GroupB editor access for all datasets in a project.
Suggested answer: C

Explanation:

Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.

Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles

asked 18/09/2024
Vidana Weerasinghe
43 questions

Question 59

Report
Export
Collapse

Which of these statements about BigQuery caching is true?

By default, a query's results are not cached.
By default, a query's results are not cached.
BigQuery caches query results for 48 hours.
BigQuery caches query results for 48 hours.
Query results are cached even if you specify a destination table.
Query results are cached even if you specify a destination table.
There is no charge for a query that retrieves its results from cache.
There is no charge for a query that retrieves its results from cache.
Suggested answer: D

Explanation:

When query results are retrieved from a cached results table, you are not charged for the query.

BigQuery caches query results for 24 hours, not 48 hours.

Query results are not cached if you specify a destination table.

A query's results are always cached except under certain conditions, such as if you specify a destination table.

Reference: https://cloud.google.com/bigquery/querying-data#query-caching

asked 18/09/2024
Istvan Flach
30 questions

Question 60

Report
Export
Collapse

Which of these sources can you not load data into BigQuery from?

File upload
File upload
Google Drive
Google Drive
Google Cloud Storage
Google Cloud Storage
Google Cloud SQL
Google Cloud SQL
Suggested answer: D

Explanation:

You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to

BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.

Reference: https://cloud.google.com/bigquery/loading-data

asked 18/09/2024
Crystal Eagle
31 questions
Total 377 questions
Go to page: of 38
Open VPLUS File
Convert VPLUS to PDF

Related questions

You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups: * Data engineers, which require lull data lake access * Analytic users, which require access to curated data You need to assign access rights to these two groups. What should you do?
You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SOL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?
An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training dat a. However, when tested against new data, it performs poorly. What method can you employ to address this?
You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?
One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re-encrypt all of your CMEK-protected Cloud Storage data that used that key. and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK protection in the future. What should you do?
What are all of the BigQuery operations that Google charges for?
You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?
You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?
You have a streaming pipeline that ingests data from Pub/Sub in production. You need to update this streaming pipeline with improved business logic. You need to ensure that the updated pipeline reprocesses the previous two days of delivered Pub/Sub messages. What should you do? Choose 2 answers