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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection - Designing for business requirements 1. Designing for scalability and elasticity 2. Designing for reliability and fault tolerance 3. Selecting appropriate storage solutions
Topic 2: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads
Topic 3: Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Ingesting data from various sources 2. Transforming and cleaning data 3. Orchestrating data workflows
Topic 4: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options
Topic 5: Operationalizing machine learning models	20%	- Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data - Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring

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Google Certified Professional Data Engineer Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. HiveQL using Hive
B. Java using MapReduce
C. PigLatin using Pig
D. Python using MapReduce

Answer: C

Explanation:
Pig is scripting language which can be used for checkpointing and splitting pipelines.

NEW QUESTION # 153
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

A. WHERE
B. LIMIT
C. SELECT
D. BETWEEN

Answer: C

Explanation:
Explanation
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference:
https://cloud.google.com/bigquery/launch-checklist#architecture_design_and_development_checklist

NEW QUESTION # 154
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A. They have not assigned the timestamp, which causes the job to fail
B. They have not applied a global windowing function, which causes the job to fail when the pipeline is
created
C. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
D. They have not set the triggers to accommodate the data coming in late, which causes the job to fail

Answer: B

NEW QUESTION # 155
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in- time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment.
You want to archive these snapshots for a long time. Which two methods can accomplish this?
(Choose two.)

A. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
B. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
D. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
E. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.

Answer: A,D

Explanation:
https://cloud.google.com/datastore/docs/export-import-entities

NEW QUESTION # 156
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = `1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

A. `bigquery-public-data.noaa_gsod.gsod'*
B. `bigquery-public-data.noaa_gsod.gsod`
C. `bigquery-public-data.noaa_gsod.gsod*`
D. bigquery-public-data.noaa_gsod.gsod*

Answer: C

Explanation:
It follows the correct wildcard syntax of enclosing the table name in backticks and including the * wildcard character.

NEW QUESTION # 157
......

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