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Google Certified Professional Data Engineer Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

A. run parallel instances where one is HDD and the other is SDD
B. create a third instance and sync the data from the two storage types via batch jobs
C. the selection is final and you must resume using the same storage type
D. export the data from the existing instance and import the data into a new instance

Answer: D

Explanation:
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write
a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.

NEW QUESTION # 204
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
B. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
C. Create a numeric column from a feature cross of latitude and longitude.
D. Provide latitude and longitude as input vectors to your neural net.

Answer: C

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

NEW QUESTION # 205
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. YARN
B. Pig
C. Hive
D. Spark

Answer: B,C,D

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.

NEW QUESTION # 206
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non- key columns. What should you do?

A. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 207
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

A. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
B. Use .fromQuery operation to read specific fields from the table.
C. Specify the TableReference object in the code.
D. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Answer: A

NEW QUESTION # 208
......

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