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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
B. Create a cron schedule in Cloud Dataprep.
C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Answer: C

Explanation:
Explanation

NEW QUESTION # 61
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

A. Ignite
B. Spark
C. Fire
D. Blaze

Answer: B

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.

NEW QUESTION # 62
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Windows
B. Sessions
C. OutputCriteria
D. Triggers

Answer: D

Explanation:
Explanation
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Trig

NEW QUESTION # 63
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

A. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
B. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
C. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
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: A

Explanation:
Bigquery is used to analyse access logs, data access logs capture the details of the user that accessed the data.

NEW QUESTION # 64
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery.
What should you do?

A. Re-create the table using data partitioning on the package delivery date.
B. Implement clustering in BigQuery on the package-tracking ID column.
C. Implement clustering in BigQuery on the ingest date column.
D. Tier older data onto Google Cloud Storage files and create a BigQuery table using GCS as an external data source.

Answer: B

NEW QUESTION # 65
......

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