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NEW QUESTION: 1
Which of the following phenomenon results in decreased signal levels due to passing through a solid structure?
A. Reflection
B. Absorption
C. Refraction
D. Attenuation
Answer: B

NEW QUESTION: 2
Which three statements about the TACACS protocol are correct? (Choose three.)
A. TACACS+ uses TCP port 47 by default.
B. TACACS+ only encrypts the password of the user for security.
C. TACACS+ is considered to be more secure than the RADIUS protocol.
D. TACACS+ is an IETF standard protocol.
E. TACACS+ can support authorization and accounting while having another separate authentication solution.
F. TACACS+ supports per-user or per-group for authorization of router commands.
Answer: C,E,F
Explanation:
RADIUS uses UDP while TACACS+ uses TCP. TCP offers several advantages over UDP. TCP offers a connection-oriented transport, while UDP offers best-effort delivery. RADIUS requires additional programmable variables such as re-transmit attempts and time-outs to compensate for best-effort transport, but it lacks the level of built-in support that a TCP transport offers:
- TCP usage provides a separate acknowledgment that a request has been received, within (approximately) a network round-trip time (RTT), regardless of how loaded and slow the backend authentication mechanism (a TCP acknowledgment) might be.
- TCP provides immediate indication of a crashed, or not running, server by a reset (RST). You can determine when a server crashes and returns to service if you use long-lived TCP connections. UDP cannot tell the difference between a server that is down, a slow server, and a non-existent server.
- Using TCP keepalives, server crashes can be detected out-of-band with actual
requests. Connections to multiple servers can be maintained simultaneously, and
you only need to send messages to the ones that are known to be up and running.
- TCP is more scalable and adapts to growing, as well as congested, networks. RADIUS combines authentication and authorization. The access-accept packets sent by the RADIUS server to the client contain authorization information. This makes it difficult to decouple authentication and authorization. TACACS+ uses the AAA architecture, which separates AAA. This allows separate authentication solutions that can still use TACACS+ for authorization and accounting. For example, with TACACS+, it is possible to use Kerberos authentication and TACACS+ authorization and accounting. After a NAS authenticates on a Kerberos server, it requests authorization information from a TACACS+ server without having to re-authenticate. The NAS informs the TACACS+ server that it has successfully authenticated on a Kerberos server, and the server then provides authorization information. During a session, if additional authorization checking is needed, the access server checks with a TACACS+ server to determine if the user is granted permission to use a particular command. This provides greater control over the commands that can be executed on the access server while decoupling from the authentication mechanism. TACACS+ provides two methods to control the authorization of router commands on a peruser or per-group basis. The first method is to assign privilege levels to commands and have the router verify with the TACACS+ server whether or not the user is authorized at the specified privilege level. The second method is to explicitly specify in the TACACS+ server, on a per-user or per-group basis, the commands that are allowed.

NEW QUESTION: 3
組織には、1日あたり10 GBのテレメトリデータを生成する10,000個のデバイスがあり、各レコードサイズは約10 KBです。各レコードには100個のフィールドがあり、1つのフィールドは、英語の「String」データ型の非構造化ログデータで構成されています。一部のフィールドはリアルタイムダッシュボードに必要ですが、すべてのフィールドは長期的な生成に使用できる必要があります。
また、組織は、1か月以内にAWSに移行する必要があるSAN内に、日付ごとにパーティション分割された10 PBの以前にクリーン化および構造化されたデータを持っています。現在、組織にはソリューションにリアルタイム機能がありません。オンプレミスデータウェアハウスのストレージの制限のため、選択データがロードされ、同時に視覚化のためのJDBCを介したANSI SQLクエリで長期トレンドが生成されます。 1回限りのデータ読み込みに加えて、組織には費用対効果の高いリアルタイムソリューションが必要です。
これらの要件をどのように満たすことができますか？ （2つ選択してください。）
A. AWS IoTを使用して、IoTルールエンジンを使用してデバイスからAmazon Kinesis Data Streamsにデータを送信します。
Kinesisストリームに接続されたKinesis Data Firehoseストリームを1つ使用して、日付ごとにパーティション分割されたデータをバッチ処理およびストリーミングします。同じKinesisストリームに接続された別のKinesis Firehoseストリームを使用して、リアルタイム分析のためにElasticsearchに取り込む必要なフィールドをフィルターで除外します。
B. AWS IoTを使用して、IoTルールエンジンを使用してデバイスからAmazon Kinesis Data Streamsにデータを送信します。
Kinesisストリームに接続された1つのKinesis Data Firehoseストリームを使用して、日付ごとにパーティション分割されたAmazon S3バケットにデータをストリーミングします。同じKinesisストリームを使用してAWS Lambda関数をアタッチし、リアルタイム分析のためにAmazon DynamoDBに取り込むために必要なフィールドを除外します。
C. AWSとオンプレミスデータセンターの間にダイレクトコネクト接続を作成し、S3アクセラレーションを使用してデータをAmazon S3にコピーします。 Amazon Athenaを使用してデータをクエリします。
D. AWS IoTを使用して、デバイスからAmazon SQSキューにデータを送信し、Auto Scalingグループに一連のワーカーを作成し、キューからバッチでレコードを読み取り、データを処理して保存します。 AWS Lambda関数が添付されたAmazon SNSキューにファンアウトして、リクエストデータセットをフィルター処理し、リアルタイム分析のためにAmazon Elasticsearch Serviceに保存します。
E. 複数のAWS Snowball Edgeデバイスを使用してデータをAmazon S3に転送し、Amazon Athenaを使用してデータをクエリします。
Answer: B,E