NEW ASSOCIATE-DATA-PRACTITIONER DUMPS PPT - EXAM ASSOCIATE-DATA-PRACTITIONER PASS GUIDE

New Associate-Data-Practitioner Dumps Ppt - Exam Associate-Data-Practitioner Pass Guide

New Associate-Data-Practitioner Dumps Ppt - Exam Associate-Data-Practitioner Pass Guide

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Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 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
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 (Q30-Q35):

NEW QUESTION # 30
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

  • A. ELT
  • B. ETL
  • C. EL
  • D. ETLT

Answer: B

Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.


NEW QUESTION # 31
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). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.
  • B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
  • 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) with asynchronous replication to a secondary instance in a different region.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:
* Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post- disaster. Primary operations remain in one zone, minimizing latency.
* Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour-too high for valuable data. Manual backup management adds overhead.
* Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.


NEW QUESTION # 32
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

  • A. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
  • B. Use Vertex AI Workbench to develop a custom model for purchase prediction.
  • C. Use Colab Enterprise to develop a custom model for purchase prediction.
  • D. Use BigQuery ML to create a logistic regression model for purchase prediction.

Answer: D

Explanation:
Using BigQuery ML is the best solution in this case because:
Ease of use: BigQuery ML allows users to build machine learning models using SQL, which requires minimal ML expertise.
Integrated platform: Since the data already exists in BigQuery, there's no need to move it to another service, saving time and engineering resources.
Logistic regression: This is an appropriate model for binary classification tasks like predicting the likelihood of a customer making a purchase in the next month.


NEW QUESTION # 33
Your retail company collects customer data from various sources:
You are designing a data pipeline to extract this dat
a. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

  • A. 1. Online transactions: Cloud SQL for MySQL
    2. Customer feedback: BigQuery
    3. Social media activity: Cloud Storage
  • B. 1. Online transactions: Cloud Storage
    2. Customer feedback: Cloud Storage
    3. Social media activity: Cloud Storage
  • C. 1. Online transactions: BigQuery
    2. Customer feedback: Cloud Storage
    3. Social media activity: BigQuery
  • D. 1. Online transactions: Bigtable
    2. Customer feedback: Cloud Storage
    3. Social media activity: CloudSQL for MySQL

Answer: C

Explanation:
Online transactions: Storing the transactional data in BigQuery is ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback: Storing customer feedback in Cloud Storage is appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity: Storing real-time social media activity in BigQuery is optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.


NEW QUESTION # 34
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

  • A. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
  • B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
  • C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
  • D. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.

Answer: A

Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.


NEW QUESTION # 35
......

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