Professional-Data-Engineer Certification Practice - Professional-Data-Engineer Actual Exam Dumps

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Preparing and using data for analysis (~15% of the exam)	15%	- Sharing data securely 1. Data sharing and collaboration 2. Publishing datasets - Preparing data for visualization 1. Connecting to Looker and other BI tools 2. Preparing data for reporting and dashboards
Designing data processing systems (~30% of the exam)	30%	- Designing data pipelines 1. Streaming (e.g., windowing, late arriving data) 2. Batch processing 3. AI data enrichment 4. Data acquisition and import 5. Processing logic 6. Integrating with new data sources - Designing data processing resources 1. Cost optimization 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cluster sizing and autoscaling - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements
Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Automating data processes 1. Scheduling jobs 2. Workflow orchestration 3. Continuous integration and continuous deployment (CI/CD) - Designing for reliability and fidelity 1. Recovering from failures 2. Planning for monitoring and alerting 3. Performing data quality and validation checks
Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Data encryption 2. Auditing, privacy, and compliance 3. Identity and Access Management (IAM) - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle - Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines
Storing the data (~20% of the exam)	20%	- Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Deciding the degree of data normalization 3. Designing the data model 4. Mapping business requirements - Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance - Using a data lake 1. Monitoring the data lake 2. Processing data 3. Managing the lake (data discovery, access, cost controls) - Designing for a data platform 1. Building a federated governance model for distributed data systems 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage

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Google Certified Professional Data Engineer Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

A. Use a cached query to accelerate time to results.
B. Create a materialized view based off of the query being run.
C. Run a scheduled query to pull the necessary data at specific intervals daily.
D. Limit the query columns being pulled in the final result.

Answer: C

Explanation:
Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base tables change, so they always return fresh data. Materialized views can also be used by the BigQuery optimizer to process queries to the base tables, if any part of the query can be resolved by querying the materialized view. References:
* Introduction to materialized views
* Create materialized views
* BigQuery Materialized View Simplified: Steps to Createand 3 Best Practices
* Materialized view in Bigquery

NEW QUESTION # 135
You are building a teal-lime prediction engine that streams files, which may contain Pll (personal identifiable information) data, into Cloud Storage and eventually into BigQuery You want to ensure that the sensitive data is masked but still maintains referential Integrity, because names and emails are often used as join keys How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the Pll data is not accessible by unauthorized individuals?

A. Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non-tokenized data in a locked-down button.
B. Scan every table in BigQuery, and mask the data it finds that has Pll
C. Redact all Pll data, and store a version of the unredacted data in a locked-down bucket
D. Create a pseudonym by replacing Pll data with a cryptographic format-preserving token

Answer: A

NEW QUESTION # 136
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. Implement clustering in BigQuery on the package-tracking ID column.
B. Re-create the table using data partitioning on the package delivery date.
C. Implement clustering in BigQuery on the ingest date column.
D. Tier older data onto Cloud Storage files, and leverage extended tables.

Answer: C

NEW QUESTION # 137
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

A. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs
B. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
C. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
D. Export the information to Cloud Stackdriver, and set up an Alerting policy

Answer: B

NEW QUESTION # 138
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

A. Install the StackDriver Agent and configure the MySQL plugin.
B. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
C. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
D. Place the MariaDB instances in an Instance Group with a Health Check.

Answer: C

NEW QUESTION # 139
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