Associate-Data-Practitioner Prüfungsaufgaben & Associate-Data-Practitioner Lerntipps

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Google Associate-Data-Practitioner Prüfungsplan:

Thema	Einzelheiten
Thema 1	Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Thema 2	Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services
Thema 3	Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.\| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.

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Associate-Data-Practitioner Fragen & Antworten & Associate-Data-Practitioner Studienführer & Associate-Data-Practitioner Prüfungsvorbereitung

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Google Cloud Associate Data Practitioner Associate-Data-Practitioner Prüfungsfragen mit Lösungen (Q11-Q16):

11. Frage
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
B. Require users to specify a partition filter using the alter table statement in SQL.
C. Create a view that filters out rows that are older than one year.
D. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Antwort: D

Begründung:
Setting the table partition expiration period to one year using the ALTER TABLE statement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.

12. Frage
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
C. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Antwort: D

Begründung:
Using aCloud Run functiontriggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.
The goal is to load small CSV files into BigQuery upon arrival (event-driven) with minimal latency, cost, and maintenance. Google Cloud provides serverless, event-driven options that align with this requirement. Let's evaluate each option in detail:
Option A: Cloud Composer (managed Apache Airflow) can schedule a pipeline to check Cloud Storage every
10 minutes, but this polling approach introduces latency (up to 10 minutes) and incurs costs for running Composer even when no files arrive. Maintenance includes managing DAGs and the Composer environment, which adds overhead. This is better suited for scheduled batch jobs, not event-driven ingestion.
Option B: A Cloud Run function triggered by a Cloud Storage event (via Eventarc or Pub/Sub) loads files into BigQuery as soon as they arrive, minimizing latency. Cloud Run is serverless, scales to zero when idle (low cost), and requires minimal maintenance (deploy and forget). Using the BigQuery API in the function (e.g., Python client library) handles small CSV loads efficiently. This aligns with Google's serverless, event-driven best practices.
Option C: Dataproc with Spark is designed for large-scale, distributed processing, not small CSV ingestion. It requires cluster management, incurs higher costs (even with ephemeral clusters), and adds unnecessary complexity for a simple load task.
Option D: The bq command-line tool in Cloud Shell is manual and not automated, failing the "upon arrival" requirement. It's a one-off tool, not a pipeline solution, and Cloud Shell isn't designed for persistent automation.
Why B is Best: Cloud Run leverages Cloud Storage's object creation events, ensuring near-zero latency between file arrival and BigQuery ingestion. It's serverless, meaning no infrastructure to manage, and costs scale with usage (free when idle). For small CSVs, the BigQuery load job is lightweight, avoiding processing overhead.
Extract from Google Documentation: From "Triggering Cloud Run with Cloud Storage Events" (https://cloud.
google.com/run/docs/triggering/using-events): "You can trigger Cloud Run services in response to Cloud Storage events, such as object creation, using Eventarc. This serverless approach minimizes latency and maintenance, making it ideal for real-time data pipelines." Additionally, from "Loading Data into BigQuery" (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv): "Programmatically load CSV files from Cloud Storage using the BigQuery API, enabling automated ingestion with minimal overhead."

13. Frage
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

A. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
C. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
D. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.

Antwort: A

Begründung:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Synchronous replication ensures that data is written to both the primary and secondary instances at the same time, minimizing data loss (RPO).
Regional availability (HA) within different zones provides redundancy within the same region, minimizing latency.
Why other options are incorrect:A: Asynchronous replication has a potential for data loss.
B: Multiregion backups are for disaster recovery, not minimizing latency.
C: Hourly backups do not provide the lowest possible RPO.

14. Frage
Your organization's business analysts require near real-time access to streaming dat a. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

A. Create materialized views.
B. Create a scheduled query to calculate and store intermediate results.
C. Modify the schema to use parameterized data types.
D. Disable BiqQuery query result caching.

Antwort: A

Begründung:
Creating materialized views is the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.

15. Frage
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model. What should you do?

A. Compare the two different models.
B. Evaluate the data skewness.
C. Compare the confusion matrix.
D. Evaluate data drift.

Antwort: D

Begründung:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.

16. Frage
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Associate-Data-Practitioner Lerntipps: https://www.zertpruefung.ch/Associate-Data-Practitioner_exam.html

Außerdem sind jetzt einige Teile dieser ZertPruefung Associate-Data-Practitioner Prüfungsfragen kostenlos erhältlich: https://drive.google.com/open?id=1zZ2AqaU92VSaFAVJriCfgWA5eBVY6ZDm