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Google Certified Professional Data Engineer Exam Sample Questions (Q190-Q195):

NEW QUESTION # 190
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:
What is the most likely cause of the delay for this query?

A. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
B. Users are running too many concurrent queries in the system
C. Either the state or the city columns in the [myproject:mydataset.mytable] table have too manyNULL values
D. The [myproject:mydataset.mytable] table has too many partitions

Answer: B

NEW QUESTION # 191
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

A. categorical_column_with_hash_bucket
B. categorical_column_with_vocabulary_list
C. categorical_column_with_unknown_values
D. sparse_column_with_keys

Answer: A

Explanation:
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.

NEW QUESTION # 192
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

A. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery
B. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
C. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
D. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.

Answer: A

NEW QUESTION # 193
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.)

A. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.
B. Introduce data compression for each file to increase the rate file of file transfer.
C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
D. 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.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Answer: A,C

Explanation:
Explanation/Reference:

NEW QUESTION # 194
Which of these sources can you not load data into BigQuery from?

A. File upload
B. Google Drive
C. Google Cloud Storage
D. Google Cloud SQL

Answer: D

Explanation:
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

NEW QUESTION # 195
......

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