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Google Associate-Data-Practitioner Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Preparation and Data Processing- Transforming data
  • 1. Data wrangling and enrichment
  • 2. Using SQL for data transformation (e.g., BigQuery)
  • 3. Using Apache Beam/Dataflow for data transformation
  • 4. Data cleansing and preprocessing
- Ingesting data
  • 1. Streaming data ingestion (e.g., Pub/Sub, Dataflow)
  • 2. Data pipeline orchestration (e.g., Cloud Composer, Workflows)
  • 3. Batch data ingestion (e.g., Cloud Storage, Transfer Service, Datastream)
Topic 2: Machine Learning- Machine learning fundamentals
  • 1. Feature engineering and selection
  • 2. Choosing appropriate ML models
  • 3. Understanding ML concepts and workflows
- Implementing ML solutions
  • 1. Training and evaluating ML models
  • 2. AutoML and custom training
  • 3. ML model deployment and serving
  • 4. Using Vertex AI for ML workflows
Topic 3: Data Analysis and Data Visualization- Visualizing data
  • 1. Data visualization best practices
  • 2. Creating dashboards and reports (e.g., Looker, Looker Studio)
  • 3. Sharing and collaboration of data insights
- Analyzing data
  • 1. Using BigQuery for data analysis
  • 2. Performing data exploration and analysis
  • 3. Statistical analysis and data modeling
  • 4. Using Looker for data analysis
Topic 4: Data Management and Governance- Data governance and quality
  • 1. Data cataloging and metadata management (e.g., Data Catalog)
  • 2. Data lineage and provenance
  • 3. Regulatory compliance and data privacy
  • 4. Data quality monitoring and validation
- Data storage and management
  • 1. Data lifecycle management
  • 2. Choosing appropriate data storage solutions (e.g., BigQuery, Cloud Storage, Firestore, Bigtable)
  • 3. Data warehouse and data lake design

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Google Cloud Associate Data Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular. You need to design a storage system that is simple and cost-effective. What should you do?

Answer: C

Explanation:
The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing over time, with some files remaining popular. Google Cloud Storage offers tailored options for this:
* Option A: Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30 days, Archive after 90 days) are effective but static. They don't adapt to actual usage, so popular old files in Archive would incur high retrieval costs.
* Option B: Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive) based on object access patterns, not just age. It keeps frequently accessed files in Standard (low latency
/cost for access) and moves inactive ones to cheaper classes, minimizing costs while preserving simplicity. This fits the "some files remain popular" nuance.
* Option C: A Cloud Scheduler job to manually change classes daily is complex (requires scripting, monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM.


NEW QUESTION # 65
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

Answer: D

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.


NEW QUESTION # 66
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?

Answer: C

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 # 67
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

Answer: B

Explanation:
UsingTransfer Appliancesis the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.


NEW QUESTION # 68
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Answer: C

Explanation:
To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to the Dataflow Jobs page in the Google Cloud console. The console provides access to detailed job logs and worker logs, which can help you identify the cause of the failure. The graphical interface also allows you to visualize pipeline stages, monitor performance metrics, and pinpoint where the error occurred, making it the most efficient way to diagnose and resolve the issue promptly.


NEW QUESTION # 69
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