EXCELLENT VALID ASSOCIATE-DATA-PRACTITIONER EXAM ONLINE BY GUIDETORRENT

Excellent Valid Associate-Data-Practitioner Exam Online by GuideTorrent

Excellent Valid Associate-Data-Practitioner Exam Online by GuideTorrent

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Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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
Topic 2
  • 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.
Topic 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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Google Cloud Associate Data Practitioner Sample Questions (Q58-Q63):

NEW QUESTION # 58
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

  • A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
  • B. Apply a filter to only show products with a positive profit margin.
  • C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
  • D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Answer: A

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn't calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.


NEW QUESTION # 59
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. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
  • B. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
  • C. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
  • D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

Answer: B

Explanation:
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."


NEW QUESTION # 60
You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

  • A. Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.
  • B. Execute the bq load command on your local machine.
  • C. Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.
  • D. Write and execute a Python script using the BigQuery Storage Write API library.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.
* Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging-more effort than necessary for a one-time task.
* Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting-far too complex for a simple load.
* Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load --source_format=CSV dataset.
table file.csv). It's pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.


NEW QUESTION # 61
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

  • A. Use Cloud Data Fusion to visually design and manage the pipeline.
  • B. Use Dataflow with a custom template for the transformation logic.
  • C. Use Dataproc to run the transformations on a cluster.
  • D. Use Dataform to define the transformations in SQLX.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The pipeline must handle large-scale log processing with existing Spark scripts, prioritizing skillset reuse, scalability, and cost. Let's break it down:
* Option A: Dataflow uses Apache Beam, not Spark, requiring script rewrites (losing skillset leverage).
Custom templates scale well but increase development cost and effort.
* Option B: Cloud Data Fusion is a visual ETL tool, not Spark-based. It doesn't reuse existing scripts, requiring redesign, and is less cost-efficient for complex, code-driven transformations.
* Option C: Dataform uses SQLX for BigQuery ELT, not Spark. It's unsuitable for pre-load transformations of raw logs and doesn't leverage Spark skills.


NEW QUESTION # 62
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

  • A. Grant the appropriate 1AM permissions on the dataset.
  • B. Create a row-level access policy.
  • C. Add a policy tag in BigQuery.
  • D. Create a data masking rule.

Answer: B

Explanation:
Creating arow-level access policyin BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
Extract from Google Documentation: From "Row-Level Security in BigQuery" (https://cloud.google.com
/bigquery/docs/row-level-security):"Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user's region, providing fine-grained control over data visibility without creating separate tables or views."


NEW QUESTION # 63
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